Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data

Yin Jou Huang, Jing Lu, Sadao Kurohashi, Vincent Ng

Abstract
Argument compatibility is a linguistic condition that is frequently incorporated into modern event coreference resolution systems. If two event mentions have incompatible arguments in any of the argument roles, they cannot be coreferent. On the other hand, if these mentions have compatible arguments, then this may be used as information towards deciding their coreferent status. One of the key challenges in leveraging argument compatibility lies in the paucity of labeled data. In this work, we propose a transfer learning framework for event coreference resolution that utilizes a large amount of unlabeled data to learn argument compatibility of event mentions. In addition, we adopt an interactive inference network based model to better capture the compatible and incompatible relations between the context words of event mentions. Our experiments on the KBP 2017 English dataset confirm the effectiveness of our model in learning argument compatibility, which in turn improves the performance of the overall event coreference model.
Anthology ID:
N19-1085
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
785–795
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/N19-1085/
DOI:
10.18653/v1/N19-1085
Bibkey:
Cite (ACL):
Yin Jou Huang, Jing Lu, Sadao Kurohashi, and Vincent Ng. 2019. Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 785–795, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data (Huang et al., NAACL 2019)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/N19-1085.pdf
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