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Mismatching-Aware Unsupervised Translation Quality Estimation For Low-Resource Languages (2208.00463v3)
Published 31 Jul 2022 in cs.CL
Abstract: Translation Quality Estimation (QE) is the task of predicting the quality of machine translation (MT) output without any reference. This task has gained increasing attention as an important component in the practical applications of MT. In this paper, we first propose XLMRScore, which is a cross-lingual counterpart of BERTScore computed via the XLM-RoBERTa (XLMR) model. This metric can be used as a simple unsupervised QE method, nevertheless facing two issues: firstly, the untranslated tokens leading to unexpectedly high translation scores, and secondly, the issue of mismatching errors between source and hypothesis tokens when applying the greedy matching in XLMRScore. To mitigate these issues, we suggest replacing untranslated words with the unknown token and the cross-lingual alignment of the pre-trained model to represent aligned words closer to each other, respectively. We evaluate the proposed method on four low-resource language pairs of the WMT21 QE shared task, as well as a new English$\rightarrow$Persian (En-Fa) test dataset introduced in this paper. Experiments show that our method could get comparable results with the supervised baseline for two zero-shot scenarios, i.e., with less than 0.01 difference in Pearson correlation, while outperforming unsupervised rivals in all the low-resource language pairs for above 8%, on average.
- Banerjee S, Lavie A (2005) Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In: Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pp 65–72 Cao et al (2019) Cao S, Kitaev N, Klein D (2019) Multilingual alignment of contextual word representations. In: International Conference on Learning Representations do Carmo et al (2021) do Carmo F, Shterionov D, Moorkens J, et al (2021) A review of the state-of-the-art in automatic post-editing. Machine Translation 35(2):101–143 Chen et al (2021) Chen Y, Su C, Zhang Y, et al (2021) Hw-tsc’s participation at wmt 2021 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation, pp 890–896 Conneau and Lample (2019) Conneau A, Lample G (2019) Cross-lingual language model pretraining. Advances in neural information processing systems 32 Conneau et al (2020) Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Cao S, Kitaev N, Klein D (2019) Multilingual alignment of contextual word representations. In: International Conference on Learning Representations do Carmo et al (2021) do Carmo F, Shterionov D, Moorkens J, et al (2021) A review of the state-of-the-art in automatic post-editing. Machine Translation 35(2):101–143 Chen et al (2021) Chen Y, Su C, Zhang Y, et al (2021) Hw-tsc’s participation at wmt 2021 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation, pp 890–896 Conneau and Lample (2019) Conneau A, Lample G (2019) Cross-lingual language model pretraining. Advances in neural information processing systems 32 Conneau et al (2020) Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 do Carmo F, Shterionov D, Moorkens J, et al (2021) A review of the state-of-the-art in automatic post-editing. Machine Translation 35(2):101–143 Chen et al (2021) Chen Y, Su C, Zhang Y, et al (2021) Hw-tsc’s participation at wmt 2021 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation, pp 890–896 Conneau and Lample (2019) Conneau A, Lample G (2019) Cross-lingual language model pretraining. Advances in neural information processing systems 32 Conneau et al (2020) Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Chen Y, Su C, Zhang Y, et al (2021) Hw-tsc’s participation at wmt 2021 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation, pp 890–896 Conneau and Lample (2019) Conneau A, Lample G (2019) Cross-lingual language model pretraining. Advances in neural information processing systems 32 Conneau et al (2020) Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Conneau A, Lample G (2019) Cross-lingual language model pretraining. Advances in neural information processing systems 32 Conneau et al (2020) Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 do Carmo F, Shterionov D, Moorkens J, et al (2021) A review of the state-of-the-art in automatic post-editing. Machine Translation 35(2):101–143 Chen et al (2021) Chen Y, Su C, Zhang Y, et al (2021) Hw-tsc’s participation at wmt 2021 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation, pp 890–896 Conneau and Lample (2019) Conneau A, Lample G (2019) Cross-lingual language model pretraining. Advances in neural information processing systems 32 Conneau et al (2020) Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Chen Y, Su C, Zhang Y, et al (2021) Hw-tsc’s participation at wmt 2021 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation, pp 890–896 Conneau and Lample (2019) Conneau A, Lample G (2019) Cross-lingual language model pretraining. Advances in neural information processing systems 32 Conneau et al (2020) Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Conneau A, Lample G (2019) Cross-lingual language model pretraining. Advances in neural information processing systems 32 Conneau et al (2020) Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 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In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). 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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). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). 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European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Conneau A, Lample G (2019) Cross-lingual language model pretraining. Advances in neural information processing systems 32 Conneau et al (2020) Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. 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Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Conneau A, Lample G (2019) Cross-lingual language model pretraining. Advances in neural information processing systems 32 Conneau et al (2020) Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? 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Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Conneau A, Khandelwal K, Goyal N, et al (2020) Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, pp 8440–8451 Cuturi (2013) Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Cuturi M (2013) Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems 26 Devlin et al (2019) Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
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In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Devlin J, Chang MW, Lee K, et al (2019) 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). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186 Edelsbrunner and Morozov (2012) Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. 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Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Edelsbrunner H, Morozov D (2012) Persistent homology: Theory and practice. In: Proceedings of the European Congress of Mathematics. European Mathematical Society, pp 31–50 Etchegoyhen et al (2018) Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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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). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? 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In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Etchegoyhen T, Garcia EM, Azpeitia A (2018) Supervised and unsupervised minimalist quality estimators: Vicomtech’s participation in the wmt 2018 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 782–787 Fomicheva et al (2020) Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 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In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Fomicheva M, Sun S, Yankovskaya L, et al (2020) Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8:539–555 Fomicheva et al (2022) Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Fomicheva M, Sun S, Fonseca E, et al (2022) MLQE-PE: A multilingual quality estimation and post-editing dataset. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 4963–4974 Guzmán et al (2019) Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
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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). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Guzmán F, Chen PJ, Ott M, et al (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 6098–6111 Huang et al (2019) Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. 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In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Huang H, Liang Y, Duan N, et al (2019) Unicoder: A universal language encoder by pre-training with multiple cross-lingual tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 2485–2494 Ive et al (2018) Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Ive J, Blain F, Specia L (2018) Deepquest: a framework for neural-based quality estimation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 3146–3157 Jabbari et al (2012) Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Jabbari F, Bakshaei S, Ziabary SMM, et al (2012) Developing an open-domain english-farsi translation system using afec: Amirkabir bilingual farsi-english corpus. In: Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pp 17–23 Junczys-Dowmunt et al (2018) Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Junczys-Dowmunt M, Grundkiewicz R, Dwojak T, et al (2018) Marian: Fast neural machine translation in C++. In: Proceedings of ACL 2018, System Demonstrations. Association for Computational Linguistics, Melbourne, Australia, pp 116–121 K et al (2020) K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. 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In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. 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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). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. 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ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 K K, Wang Z, Mayhew S, et al (2020) Cross-lingual ability of multilingual BERT: an empirical study. In: 8th International Conference on Learning Representations Kepler et al (2019a) Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019a) Unbabel’s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2). Association for Computational Linguistics, Florence, Italy, pp 78–84 Kepler et al (2019b) Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. 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In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. 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Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Kepler F, Trénous J, Treviso M, et al (2019b) Openkiwi: An open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 117–122 Kim et al (2017) Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Kim H, Jung HY, Kwon H, et al (2017) Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 17(1):1–22 Kim et al (2019) Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? 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In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Kim H, Lim JH, Kim HK, et al (2019) Qe bert: bilingual bert using multi-task learning for neural quality estimation. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp 85–89 Kingma and Ba (2015) Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR Koehn et al (2007) Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. 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In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 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Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Koehn P, Hoang H, Birch A, et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. Association for Computational Linguistics, Prague, Czech Republic, pp 177–180 Koehn et al (2020) Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Koehn P, Chaudhary V, El-Kishky A, et al (2020) Findings of the WMT 2020 shared task on parallel corpus filtering and alignment. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 726–742 Kulshreshtha et al (2020) Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Kulshreshtha S, Redondo Garcia JL, Chang CY (2020) Cross-lingual alignment methods for multilingual BERT: A comparative study. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp 933–942 Kunchukuttan et al (2018) Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Kunchukuttan A, Mehta P, Bhattacharyya P (2018) The IIT Bombay English-Hindi parallel corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan Lee (2020) Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? 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Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. 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In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Lee D (2020) Two-phase cross-lingual language model fine-tuning for machine translation quality estimation. In: Proceedings of the Fifth Conference on Machine Translation, pp 1024–1028 Liu et al (2019) Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Liu Q, McCarthy D, Vulić I, et al (2019) Investigating cross-lingual alignment methods for contextualized embeddings with token-level evaluation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, Hong Kong, China, pp 33–43 Moura et al (2020) Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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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). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Moura J, Vera M, van Stigt D, et al (2020) Ist-unbabel participation in the wmt20 quality estimation shared task. In: Proceedings of the Fifth Conference on Machine Translation, pp 1029–1036 Och and Ney (2003) Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. 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In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Computational Linguistics 29(1):19–51 Papineni et al (2002) Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Papineni K, Roukos S, Ward T, et al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311–318 Ranasinghe et al (2020) Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. 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In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. 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In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
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In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Ranasinghe T, Orǎsan C, Mitkov R (2020) Transquest: Translation quality estimation with cross-lingual transformers. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 5070–5081 Sabet et al (2020) Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
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In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. 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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). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Sabet MJ, Dufter P, Yvon F, et al (2020) Simalign: High quality word alignments without parallel training data using static and contextualized embeddings. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1627–1643 Snover et al (2006) Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Snover M, Dorr B, Schwartz R, et al (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pp 223–231 Specia et al (2009) Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
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Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Turchi M, Cancedda N, et al (2009) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th annual conference of the European association for machine translation Specia et al (2013) Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Shah K, De Souza JG, et al (2013) Quest-a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp 79–84 Specia et al (2020) Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 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Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. 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Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Specia L, Blain F, Fomicheva M, et al (2020) Findings of the WMT 2020 shared task on quality estimation. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 743–764 Specia et al (2021) Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Specia L, Blain F, Fomicheva M, et al (2021) Findings of the WMT 2021 shared task on quality estimation. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 684–725 Tavakoli and Faili (2014) Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Tavakoli L, Faili H (2014) Phrase alignments in parallel corpus using bootstrapping approach. International Journal of Information and Communication Technology Research Tuan et al (2021) Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Tuan YL, El-Kishky A, Renduchintala A, et al (2021) Quality estimation without human-labeled data. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp 619–625 Wang et al (2018) Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
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Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Wang J, Fan K, Li B, et al (2018) Alibaba submission for wmt18 quality estimation task. In: Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pp 809–815 Wang et al (2021) Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang J, Wang K, Chen B, et al (2021) Qemind: Alibaba’s submission to the wmt21 quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, pp 948–954 Wang et al (2019) Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
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Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. 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In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. 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- Wang Y, Che W, Guo J, et al (2019) Cross-lingual BERT transformation for zero-shot dependency parsing. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5721–5727 Wu and Dredze (2019) Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Wu S, Dredze M (2019) Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 833–844 Wu and Dredze (2020) Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. 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Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. 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- Wu S, Dredze M (2020) Do explicit alignments robustly improve multilingual encoders? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 4471–4482 Zerva et al (2021) Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Zerva C, van Stigt D, Rei R, et al (2021) IST-unbabel 2021 submission for the quality estimation shared task. In: Proceedings of the Sixth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 961–972 Zerva et al (2022) Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zerva C, Blain F, Rei R, et al (2022) Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the Seventh Conference on Machine Translation (WMT). Association for Computational Linguistics, pp 69–99 Zhang et al (2020) Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhang T, Kishore V, Wu F, et al (2020) Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations Zhou et al (2020) Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074 Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
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- Zhou L, Ding L, Takeda K (2020) Zero-shot translation quality estimation with explicit cross-lingual patterns. In: Proceedings of the Fifth Conference on Machine Translation. Association for Computational Linguistics, Online, pp 1068–1074
- Fatemeh Azadi (1 paper)
- Heshaam Faili (22 papers)
- Mohammad Javad Dousti (17 papers)