Abstract
We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-train the entity coreference model in the multi-task framework on the large amount of publicly available coreference data; and (3) integrating prior knowledge encoded in rule-based resolvers. Our approach achieves state-of-the-art results on three standard evaluation corpora.- Anthology ID:
- 2022.acl-long.56
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 759–770
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.56
- DOI:
- 10.18653/v1/2022.acl-long.56
- Cite (ACL):
- Hideo Kobayashi, Yufang Hou, and Vincent Ng. 2022. Constrained Multi-Task Learning for Bridging Resolution. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 759–770, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Constrained Multi-Task Learning for Bridging Resolution (Kobayashi et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.acl-long.56.pdf
- Code
- juntaoy/dali-bridging
- Data
- BASHI, ISNotes