Abstract
We propose a neural event coreference model in which event coreference is jointly trained with five tasks: trigger detection, entity coreference, anaphoricity determination, realis detection, and argument extraction. To guide the learning of this complex model, we incorporate cross-task consistency constraints into the learning process as soft constraints via designing penalty functions. In addition, we propose the novel idea of viewing entity coreference and event coreference as a single coreference task, which we believe is a step towards a unified model of coreference resolution. The resulting model achieves state-of-the-art results on the KBP 2017 event coreference dataset.- Anthology ID:
- 2021.naacl-main.356
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4504–4514
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.356
- DOI:
- 10.18653/v1/2021.naacl-main.356
- Cite (ACL):
- Jing Lu and Vincent Ng. 2021. Constrained Multi-Task Learning for Event Coreference Resolution. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4504–4514, Online. Association for Computational Linguistics.
- Cite (Informal):
- Constrained Multi-Task Learning for Event Coreference Resolution (Lu & Ng, NAACL 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.naacl-main.356.pdf
- Code
- samlee946/cmtl-event-coref