Abstract
While joint models have been developed for many NLP tasks, the vast majority of event coreference resolvers, including the top-performing resolvers competing in the recent TAC KBP 2016 Event Nugget Detection and Coreference task, are pipeline-based, where the propagation of errors from the trigger detection component to the event coreference component is a major performance limiting factor. To address this problem, we propose a model for jointly learning event coreference, trigger detection, and event anaphoricity. Our joint model is novel in its choice of tasks and its features for capturing cross-task interactions. To our knowledge, this is the first attempt to train a mention-ranking model and employ event anaphoricity for event coreference. Our model achieves the best results to date on the KBP 2016 English and Chinese datasets.- Anthology ID:
- P17-1009
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 90–101
- Language:
- URL:
- https://aclanthology.org/P17-1009
- DOI:
- 10.18653/v1/P17-1009
- Cite (ACL):
- Jing Lu and Vincent Ng. 2017. Joint Learning for Event Coreference Resolution. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 90–101, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Joint Learning for Event Coreference Resolution (Lu & Ng, ACL 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P17-1009.pdf