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        <title>Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering</title>
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        <namePart type="given">Rui</namePart>
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        <namePart type="family">Nogueira dos Santos</namePart>
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    <name type="personal">
        <namePart type="given">Michihiro</namePart>
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    <name type="personal">
        <namePart type="given">Bing</namePart>
        <namePart type="family">Xiang</namePart>
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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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 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.</abstract>
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%0 Conference Proceedings
%T Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering
%A Zhang, Rui
%A Nogueira dos Santos, Cícero
%A Yasunaga, Michihiro
%A Xiang, Bing
%A Radev, Dragomir
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhang-etal-2018-neural-coreference
%X 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.
%R 10.18653/v1/P18-2017
%U https://aclanthology.org/P18-2017
%U https://doi.org/10.18653/v1/P18-2017
%P 102-107

Markdown (Informal)

[Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering](https://aclanthology.org/P18-2017) (Zhang et al., ACL 2018)

• Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering (Zhang et al., ACL 2018)

ACL

• Rui Zhang, Cícero Nogueira dos Santos, Michihiro Yasunaga, Bing Xiang, and Dragomir Radev. 2018. Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 102–107, Melbourne, Australia. Association for Computational Linguistics.