Abstract
Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this work, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference-resolution datasets. We explore how the impact varies depending upon the type of mention.- Anthology ID:
- 2021.naacl-main.130
- 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:
- 1645–1651
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.130
- DOI:
- 10.18653/v1/2021.naacl-main.130
- Cite (ACL):
- Sopan Khosla, James Fiacco, and Carolyn Rosé. 2021. Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1645–1651, Online. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance (Khosla et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2021.naacl-main.130.pdf