Abstract
Current end-to-end coreference resolution models combine detection of singleton mentions and antecedent linking into a single step. In contrast, singleton detection was often treated as a separate step in the pre-neural era. In this work, we show that separately parameterizing these two sub-tasks also benefits end-to-end neural coreference systems. Specifically, we add a singleton detector to the coarse-to-fine (C2F) coreference model, and design an anaphoricity-aware span embedding and singleton detection loss. Our method significantly improves model performance on OntoNotes and four additional datasets.- Anthology ID:
- 2024.naacl-short.19
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 212–219
- Language:
- URL:
- https://aclanthology.org/2024.naacl-short.19
- DOI:
- 10.18653/v1/2024.naacl-short.19
- Cite (ACL):
- Xiyuan Zou, Yiran Li, Ian Porada, and Jackie Cheung. 2024. Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 212–219, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution (Zou et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-short.19.pdf