CorefQA: Coreference Resolution as Query-based Span Prediction

Wei Wu, Fei Wang, Arianna Yuan, Fei Wu, Jiwei Li


Abstract
In this paper, we present CorefQA, an accurate and extensible approach for the coreference resolution task. We formulate the problem as a span prediction task, like in question answering: A query is generated for each candidate mention using its surrounding context, and a span prediction module is employed to extract the text spans of the coreferences within the document using the generated query. This formulation comes with the following key advantages: (1) The span prediction strategy provides the flexibility of retrieving mentions left out at the mention proposal stage; (2) In the question answering framework, encoding the mention and its context explicitly in a query makes it possible to have a deep and thorough examination of cues embedded in the context of coreferent mentions; and (3) A plethora of existing question answering datasets can be used for data augmentation to improve the model’s generalization capability. Experiments demonstrate significant performance boost over previous models, with 83.1 (+3.5) F1 score on the CoNLL-2012 benchmark and 87.5 (+2.5) F1 score on the GAP benchmark.
Anthology ID:
2020.acl-main.622
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6953–6963
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.622
DOI:
10.18653/v1/2020.acl-main.622
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.622.pdf
Video:
 http://slideslive.com/38928701