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arXiv · 1805.04893

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

Rui Zhang·Cicero Nogueira dos Santos·Michihiro Yasunaga·Bing Xiang·Dragomir Radev
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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 the mention clustering log-likelihood given the mention cluster labels. Our model achieves the state-of-the-art performance on the CoNLL-2012 Shared Task English test set.

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Rui Zhang, Cicero Nogueira dos Santos, Michihiro Yasunaga, Bing Xiang, Dragomir Radev. 2018-05-13. Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering. https://arxiv.org/abs/1805.04893

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