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home / papers / 2023 / 2*n is better than n²: Decomposing Event Coreference Resolution into Two Tractable Problems

2*n is better than n²: Decomposing Event Coreference Resolution into Two Tractable Problems

Summary

Event Coreference Resolution (ECR) decides whether two event mentions, in the same document or in different ones, describe the same happening. Standard pairwise systems score all n² mention pairs. Most of those pairs are clearly unrelated, so training data is heavily imbalanced and computation becomes impractical for large collections. The authors split ECR into two cheaper stages. First, a lemma-based heuristic (LH) built with spaCy discards most obviously non-coreferent pairs. It compares trigger lemmas, a list of trigger lemma pairs that co-occur in training coreference chains, and lemma overlap of the mention sentences. Second, a cross-encoder discriminator, following the CDLM approach of Caciularu et al. (2021), is trained only on the surviving, roughly balanced pairs. On ECB+ and the Gun Violence Corpus (GVC), this pipeline performs about as well as state-of-the-art systems while using far less compute. An oracle variant (LH Ora) gives an upper bound on ECB+. The pruning also makes a detailed error analysis of hard pairs feasible. This suggests that scalable, lower-compute ECR is achievable.

Key points

  • Mention pairs are grouped into four types by surface similarity: P⁺easy, P⁺FN, P⁻hard and P⁻TN. The imbalance comes almost entirely from P⁻TN, which the heuristic filters out. The cross-encoder then trains on the remaining pairs, mainly P⁺easy and P⁻hard.

  • The heuristic uses spaCy en_core_web_md v3.4. A pair passes if:

    • its head lemmas match,
    • one trigger contains the other's lemma, or
    • the lemma pair is in a learned "synonym" set.

    The pair is then predicted coreferent if overlap between the stop-word-free, lemmatized sentences exceeds a threshold tuned on the dev set.

  • Data:

    • ECB+ uses topics 1–35 for train/dev and 36–45 for test, with 1,780 test mentions.
    • GVC uses the Bugert et al. (2021) split, with 1,008 test mentions, and only pairs within the same sub-topic are considered.

  • The error analysis of D small identifies these error types:

    • same-sentence pronouns,
    • weak temporal reasoning,
    • misleading lexical similarity, such as "British hiker" vs. "New Zealand hiker",
    • set-member confusions.

    Singletons, by contrast, end up in the purest clusters.

  • Limitations: experiments cover English only and use gold mentions, and Longformer training still requires high-end GPUs. Code is at github.com/ahmeshaf/lemma_ce_coref.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Coreference resolution

Cited by

13 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 9 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.findings-acl.100 · arXiv 2305.05672

@inproceedings{ahmed20232,
  title = {{2*n is better than n²: Decomposing Event Coreference Resolution into Two Tractable Problems}},
  author = {Ahmed, Shafiuddin Rehan and Nath, Abhijnan and Martin, James H. and Krishnaswamy, Nikhil},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
  year = {2023},
  doi = {10.18653/v1/2023.findings-acl.100},
  eprint = {2305.05672},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2023.findings-acl.100}
}

References

38 references, 26 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Emily Allaway, Shuai Wang, and Miguel Ballesteros. 2021. Sequential cross-document coreference resolution. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4659–4671, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. find our card
  2. Amit Bagga and Breck Baldwin. 1998. Algorithms for scoring coreference chains. In In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference, pages 563–566. find our card
  3. Breck Baldwin. 1997. CogNIAC: high precision coreference with limited knowledge and linguistic resources. In Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts. find our card
  4. Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. 2021. Beit: Bert pre-training of image transformers. arXiv preprint arXiv:2106.08254. — [arXiv:2106.08254](https://arxiv.org/abs/2106.08254) find
  5. Shany Barhom, Vered Shwartz, Alon Eirew, Michael Bugert, Nils Reimers, and Ido Dagan. 2019. Revisiting joint modeling of cross-document entity and event coreference resolution. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4179–4189, Florence, Ital… find our card
  6. Cosmin Bejan and Sanda Harabagiu. 2010. Unsupervised event coreference resolution with rich linguistic features. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1412–1422, Uppsala, Sweden. Association for Computational Linguistics. find our card
  7. Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. arXiv e-prints, pages arXiv–2004. find
  8. Susan Windisch Brown, Julia Bonn, Ghazaleh Kazeminejad, Annie Zaenen, James Pustejovsky, and Martha Palmer. 2022. Semantic representations for nlp using verbnet and the generative lexicon. Frontiers in artificial intelligence, 5. find
  9. Susan Windisch Brown, Dmitriy Dligach, and Martha Palmer. 2014. Verbnet class assignment as a wsd task. Computing Meaning: Volume 4, pages 203–216. find
  10. Michael Bugert, Nils Reimers, Shany Barhom, Ido Dagan, and Iryna Gurevych. 2020. Breaking the subtopic barrier in cross-document event coreference resolution. In Text2story@ ecir, pages 23–29. find
  11. Michael Bugert, Nils Reimers, and Iryna Gurevych. 2021. Generalizing cross-document event coreference resolution across multiple corpora. Computational Linguistics, 47(3):575–614. find our card
  12. Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew Peters, Arie Cattan, and Ido Dagan. 2021. CDLM: Cross-document language modeling. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2648–2662, Punta Cana, Dominican Republic. Association for Computational Linguistics. find
  13. Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, and Ido Dagan. 2021. Cross-document coreference resolution over predicted mentions. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 5100–5107, Online. Association for Computational Linguistics. find our card
  14. Agata Cybulska and Piek Vossen. 2014. Using a sledgehammer to crack a nut? lexical diversity and event coreference resolution. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pages 4545–4552, Reykjavik, Iceland. European Language Resources Associa… find our card
  15. Agata Cybulska and Piek Vossen. 2015. Translating granularity of event slots into features for event coreference resolution. In Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation, pages 1–10, Denver, Colorado. Association for Computational Linguisti… find our card
  16. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2021. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learni… find
  17. William Held, Dan Iter, and Dan Jurafsky. 2021. Focus on what matters: Applying discourse coherence theory to cross document coreference. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1406–1417, Online and Punta Cana, Dominican Republic. Association… find our card
  18. Kian Kenyon-Dean, Jackie Chi Kit Cheung, and Doina Precup. 2018. Resolving event coreference with supervised representation learning and clustering-oriented regularization. arXiv preprint arXiv:1805.10985. find our card
  19. Heeyoung Lee, Angel Chang, Yves Peirsman, Nathanael Chambers, Mihai Surdeanu, and Dan Jurafsky. 2013. Deterministic coreference resolution based on entity-centric, precision-ranked rules. Computational linguistics, 39(4):885–916. find our card
  20. Heeyoung Lee, Marta Recasens, Angel Chang, Mihai Surdeanu, and Dan Jurafsky. 2012. Joint entity and event coreference resolution across documents. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 489–50… find our card
  21. Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022. find our card
  22. Xiaoqiang Luo. 2005. On coreference resolution performance metrics. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT ’05, page 25–32, USA. Association for Computational Linguistics. find our card
  23. Xiaoqiang Luo, Sameer Pradhan, Marta Recasens, and Eduard Hovy. 2014. An extension of BLANC to system mentions. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 24–29, Baltimore, Maryland. Association for Computational Linguis… find
  24. Yehudit Meged, Avi Caciularu, Vered Shwartz, and Ido Dagan. 2020. Paraphrasing vs coreferring: Two sides of the same coin. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4897–4907, Online. Association for Computational Linguistics. find our card
  25. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. find
  26. Nafise Sadat Moosavi, Leo Born, Massimo Poesio, and Michael Strube. 2019. Using automatically extracted minimum spans to disentangle coreference evaluation from boundary detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4168–4178, Florence,… find our card
  27. Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005. The Proposition Bank: An annotated corpus of semantic roles. Computational Linguistics, 31(1):71– 106. find our card
  28. Marten Postma, Filip Ilievski, and Piek Vossen. 2018. SemEval-2018 task 5: Counting events and participants in the long tail. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 70–80, New Orleans, Louisiana. Association for Computational Linguistics. find
  29. Sameer Pradhan, Julia Bonn, Skatje Myers, Kathryn Conger, Tim O’gorman, James Gung, Kristin Wright-bettner, and Martha Palmer. 2022. PropBank comes of Age—Larger, smarter, and more diverse. In Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, pages 278–288, Seattle, Wa… find our card
  30. Sameer Pradhan, Xiaoqiang Luo, Marta Recasens, Eduard Hovy, Vincent Ng, and Michael Strube. 2014. Scoring coreference partitions of predicted mentions: A reference implementation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pag… find our card
  31. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PM… find our card
  32. Karin Kipper Schuler. 2005. VerbNet: A broad-coverage, comprehensive verb lexicon. University of Pennsylvania. find
  33. Veselin Stoyanov and Jason Eisner. 2012. Easy-first coreference resolution. In Proceedings of COLING 2012, pages 2519–2534. find our card
  34. Marc Vilain, John Burger, John Aberdeen, Dennis Connolly, and Lynette Hirschman. 1995. A model-theoretic coreference scoring scheme. In Proceedings of the 6th Conference on Message Understanding, MUC6 ’95, page 45–52, USA. Association for Computational Linguistics. find our card
  35. Piek Vossen, Filip Ilievski, Marten Postma, and Rox-ane Segers. 2018. Don’t annotate, but validate: A data-to-text method for capturing event data. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). find
  36. Xiaodong Yu, Wenpeng Yin, and Dan Roth. 2022a. Pairwise representation learning for event coreference. In Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, pages 69–78, Seattle, Washington. Association for Computational Linguistics. find our card
  37. Xiaodong Yu, Wenpeng Yin, and Dan Roth. 2022b. Pairwise representation learning for event coreference. In Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, pages 69–78. find our card
  38. Yutao Zeng, Xiaolong Jin, Saiping Guan, Jiafeng Guo, and Xueqi Cheng. 2020. Event coreference resolution with their paraphrases and argument-aware embeddings. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3084–3094, Barcelona, Spain (Online). International C… find our card

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