Coreference Resolution with Entity Equalization

Ben Kantor, Amir Globerson


Abstract
A key challenge in coreference resolution is to capture properties of entity clusters, and use those in the resolution process. Here we provide a simple and effective approach for achieving this, via an “Entity Equalization” mechanism. The Equalization approach represents each mention in a cluster via an approximation of the sum of all mentions in the cluster. We show how this can be done in a fully differentiable end-to-end manner, thus enabling high-order inferences in the resolution process. Our approach, which also employs BERT embeddings, results in new state-of-the-art results on the CoNLL-2012 coreference resolution task, improving average F1 by 3.6%.
Anthology ID:
P19-1066
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
673–677
Language:
URL:
https://aclanthology.org/P19-1066
DOI:
10.18653/v1/P19-1066
Bibkey:
Cite (ACL):
Ben Kantor and Amir Globerson. 2019. Coreference Resolution with Entity Equalization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 673–677, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Coreference Resolution with Entity Equalization (Kantor & Globerson, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1066.pdf
Data
CoNLL-2012OntoNotes 5.0