Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art

Jing Lu, Vincent Ng


Abstract
Despite the significant progress on entity coreference resolution observed in recent years, there is a general lack of understanding of what has been improved. We present an empirical analysis of state-of-the-art resolvers with the goal of providing the general NLP audience with a better understanding of the state of the art and coreference researchers with directions for future research.
Anthology ID:
2020.emnlp-main.536
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6620–6631
Language:
URL:
https://aclanthology.org/2020.emnlp-main.536
DOI:
10.18653/v1/2020.emnlp-main.536
Bibkey:
Cite (ACL):
Jing Lu and Vincent Ng. 2020. Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6620–6631, Online. Association for Computational Linguistics.
Cite (Informal):
Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art (Lu & Ng, EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.536.pdf
Video:
 https://slideslive.com/38939372