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 . Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 2018 – 62 citations

[Paper]    
ACL

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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