Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis (2404.06392v1)

Published 9 Apr 2024 in cs.CL and cs.AI

Abstract: Cross-lingual transfer-learning is widely used in Event Extraction for low-resource languages and involves a Multilingual LLM that is trained in a source language and applied to the target language. This paper studies whether the typological similarity between source and target languages impacts the performance of cross-lingual transfer, an under-explored topic. We first focus on Basque as the target language, which is an ideal target language because it is typologically different from surrounding languages. Our experiments on three Event Extraction tasks show that the shared linguistic characteristic between source and target languages does have an impact on transfer quality. Further analysis of 72 language pairs reveals that for tasks that involve token classification such as entity and event trigger identification, common writing script and morphological features produce higher quality cross-lingual transfer. In contrast, for tasks involving structural prediction like argument extraction, common word order is the most relevant feature. In addition, we show that when increasing the training size, not all the languages scale in the same way in the cross-lingual setting. To perform the experiments we introduce EusIE, an event extraction dataset for Basque, which follows the Multilingual Event Extraction dataset (MEE). The dataset and code are publicly available.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (54)
  1. IXA pipeline: Efficient and ready to use multilingual NLP tools. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pages 3823–3828, Reykjavik, Iceland. European Language Resources Association (ELRA).
  2. Building named entity recognition taggers via parallel corpora. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
  3. Jun Araki and Teruko Mitamura. 2018. Open-domain event detection using distant supervision. In Proceedings of the 27th International Conference on Computational Linguistics, pages 878–891, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
  4. Giusepppe Attardi. 2015. Wikiextractor. https://github.com/attardi/wikiextractor.
  5. Event extraction via dynamic multi-pooling convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 167–176, Beijing, China. Association for Computational Linguistics.
  6. Collective event detection via a hierarchical and bias tagging networks with gated multi-level attention mechanisms. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1267–1276, Brussels, Belgium. Association for Computational Linguistics.
  7. Unsupervised cross-lingual representation learning at scale. CoRR, abs/1911.02116.
  8. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8440–8451, Online. Association for Computational Linguistics.
  9. Rudolf P.G. de Rijk. 1969. Is basque an s. o. v. language? Fontes Linguae Vasconum, 1(3):319–352.
  10. Event detection with trigger-aware lattice neural network. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 347–356, Hong Kong, China. Association for Computational Linguistics.
  11. Xinya Du and Claire Cardie. 2020. Event extraction by answering (almost) natural questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 671–683, Online. Association for Computational Linguistics.
  12. Dynamic global memory for document-level argument extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5264–5275, Dublin, Ireland. Association for Computational Linguistics.
  13. Multi-sentence argument linking. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8057–8077, Online. Association for Computational Linguistics.
  14. A language-independent neural network for event detection. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 66–71, Berlin, Germany. Association for Computational Linguistics.
  15. Model and data transfer for cross-lingual sequence labelling in zero-resource settings. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6403–6416, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  16. Ralph Grishman. 2019. Twenty-five years of information extraction. Natural Language Engineering, 25(6):677–692.
  17. Prashant Gupta and Heng Ji. 2009. Predicting unknown time arguments based on cross-event propagation. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 369–372, Suntec, Singapore. Association for Computational Linguistics.
  18. Cross-lingual event detection via optimized adversarial training. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5588–5599, Seattle, United States. Association for Computational Linguistics.
  19. Using cross-entity inference to improve event extraction. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1127–1136, Portland, Oregon, USA. Association for Computational Linguistics.
  20. DEGREE: A data-efficient generation-based event extraction model. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1890–1908, Seattle, United States. Association for Computational Linguistics.
  21. Multilingual generative language models for zero-shot cross-lingual event argument extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4633–4646, Dublin, Ireland. Association for Computational Linguistics.
  22. Heng Ji and Ralph Grishman. 2008. Refining event extraction through cross-document inference. In Proceedings of ACL-08: HLT, pages 254–262, Columbus, Ohio. Association for Computational Linguistics.
  23. The INCEpTION platform: Machine-assisted and knowledge-oriented interactive annotation. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 5–9, Santa Fe, New Mexico. Association for Computational Linguistics.
  24. Itziar Laka. 1996. A brief grammar of Euskara, the Basque language. Euskal Herriko Unibertsitatea, Leioa-Donostia.
  25. Guillaume Lample and Alexis Conneau. 2019. Cross-lingual language model pretraining.
  26. Cross-lingual named entity recognition using parallel corpus: A new approach using xlm-roberta alignment.
  27. Event extraction as multi-turn question answering. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 829–838, Online. Association for Computational Linguistics.
  28. KiPT: Knowledge-injected prompt tuning for event detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1943–1952, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
  29. Joint event extraction via structured prediction with global features. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 73–82, Sofia, Bulgaria. Association for Computational Linguistics.
  30. Document-level event argument extraction by conditional generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 894–908, Online. Association for Computational Linguistics.
  31. A joint neural model for information extraction with global features. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7999–8009, Online. Association for Computational Linguistics.
  32. Event extraction as machine reading comprehension. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1641–1651, Online. Association for Computational Linguistics.
  33. Dynamic prefix-tuning for generative template-based event extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5216–5228, Dublin, Ireland. Association for Computational Linguistics.
  34. Jointly multiple events extraction via attention-based graph information aggregation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1247–1256, Brussels, Belgium. Association for Computational Linguistics.
  35. Translation-based implicit annotation projection for zero-shot cross-lingual event argument extraction. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’22, page 2076–2081, New York, NY, USA. Association for Computing Machinery.
  36. Text2Event: Controllable sequence-to-structure generation for end-to-end event extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2795–2806, Online. Association for Computational Linguistics.
  37. Introducing a new dataset for event detection in cybersecurity texts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5381–5390, Online. Association for Computational Linguistics.
  38. Timothy Mckinnon and Carl Rubino. 2022. The IARPA BETTER program abstract task four new semantically annotated corpora from IARPA’s BETTER program. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3595–3600, Marseille, France. European Language Resources Association.
  39. Recent advances in natural language processing via large pre-trained language models: A survey. ACM Comput. Surv., 56(2).
  40. Joint event extraction via recurrent neural networks. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 300–309, San Diego, California. Association for Computational Linguistics.
  41. MEE: A novel multilingual event extraction dataset. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9603–9613, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  42. Gollie: Annotation guidelines improve zero-shot information-extraction.
  43. Textual entailment for event argument extraction: Zero- and few-shot with multi-source learning. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2439–2455, Seattle, United States. Association for Computational Linguistics.
  44. ZS4IE: A toolkit for zero-shot information extraction with simple verbalizations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations, pages 27–38, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.
  45. Casie: Extracting cybersecurity event information from text. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):8749–8757.
  46. CasEE: A joint learning framework with cascade decoding for overlapping event extraction. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 164–174, Online. Association for Computational Linguistics.
  47. Literary event detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3623–3634, Florence, Italy. Association for Computational Linguistics.
  48. Instructuie: Multi-task instruction tuning for unified information extraction. CoRR, abs/2304.08085.
  49. MAVEN: A Massive General Domain Event Detection Dataset. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1652–1671, Online. Association for Computational Linguistics.
  50. Trigger is not sufficient: Exploiting frame-aware knowledge for implicit event argument extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4672–4682, Online. Association for Computational Linguistics.
  51. Capturing event argument interaction via a bi-directional entity-level recurrent decoder. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 210–219, Online. Association for Computational Linguistics.
  52. EA22{}^{2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPTE: Improving consistency with event awareness for document-level argument extraction. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2649–2655, Seattle, United States. Association for Computational Linguistics.
  53. A multi-format transfer learning model for event argument extraction via variational information bottleneck. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1990–2000, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
  54. Christopher Walker and Stephanie Strassel and Julie Medero and Kazuaki Maeda. 2006. ACE 2005 Multilingual Training Corpus. Web Download. Philadelphia: Linguistic Data Consortium, ISLRN 458-031-085-383-4.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Mikel Zubillaga (2 papers)
  2. Oscar Sainz (14 papers)
  3. Ainara Estarrona (3 papers)
  4. Oier Lopez de Lacalle (19 papers)
  5. Eneko Agirre (53 papers)
Citations (2)
View on Semantic Scholar