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://aclanthology.org/N19-1085
- DOI:
- 10.18653/v1/N19-1085
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/N19-1085.pdf