Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering

Rui Zhang, Cícero Nogueira dos Santos, Michihiro Yasunaga, Bing Xiang, Dragomir Radev


Abstract
Coreference resolution aims to identify in a text all mentions that refer to the same real world entity. The state-of-the-art end-to-end neural coreference model considers all text spans in a document as potential mentions and learns to link an antecedent for each possible mention. In this paper, we propose to improve the end-to-end coreference resolution system by (1) using a biaffine attention model to get antecedent scores for each possible mention, and (2) jointly optimizing the mention detection accuracy and mention clustering accuracy given the mention cluster labels. Our model achieves the state-of-the-art performance on the CoNLL-2012 shared task English test set.
Anthology ID:
P18-2017
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
102–107
Language:
URL:
https://www.aclweb.org/anthology/P18-2017
DOI:
10.18653/v1/P18-2017
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-2017.pdf
Poster:
 P18-2017.Poster.pdf