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    <abstract>Zero pronoun resolution aims at recognizing dropped pronouns and pointing out their anaphoric mentions, while non-zero coreference resolution targets at clustering mentions referring to the same entity. Existing efforts often deal with the two problems separately regardless of their close essential correlations. In this paper, we investigate the possibility of jointly solving zero pronoun resolution and coreference resolution via a novel end-to-end neural model. Specifically, we design a gap-masked self-attention model that encodes gaps and tokens in the same space, where gaps could capture valuable contextual information according to their surrounding tokens while tokens could maintain original sequential information without disturbance. Additionally, we also propose a two-stage interaction mechanism to make full use of the exclusive relationship between zero pronouns and mentions. Our empirical study conducted on the OntoNotes 5.0 Chinese dataset shows that our model could outperform corresponding state-of-the-art approaches on both tasks.</abstract>
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%0 Conference Proceedings
%T Tackling Zero Pronoun Resolution and Non-Zero Coreference Resolution Jointly
%A Chen, Shisong
%A Gu, Binbin
%A Qu, Jianfeng
%A Li, Zhixu
%A Liu, An
%A Zhao, Lei
%A Chen, Zhigang
%Y Bisazza, Arianna
%Y Abend, Omri
%S Proceedings of the 25th Conference on Computational Natural Language Learning
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F chen-etal-2021-tackling
%X Zero pronoun resolution aims at recognizing dropped pronouns and pointing out their anaphoric mentions, while non-zero coreference resolution targets at clustering mentions referring to the same entity. Existing efforts often deal with the two problems separately regardless of their close essential correlations. In this paper, we investigate the possibility of jointly solving zero pronoun resolution and coreference resolution via a novel end-to-end neural model. Specifically, we design a gap-masked self-attention model that encodes gaps and tokens in the same space, where gaps could capture valuable contextual information according to their surrounding tokens while tokens could maintain original sequential information without disturbance. Additionally, we also propose a two-stage interaction mechanism to make full use of the exclusive relationship between zero pronouns and mentions. Our empirical study conducted on the OntoNotes 5.0 Chinese dataset shows that our model could outperform corresponding state-of-the-art approaches on both tasks.
%R 10.18653/v1/2021.conll-1.40
%U https://aclanthology.org/2021.conll-1.40
%U https://doi.org/10.18653/v1/2021.conll-1.40
%P 518-527

Markdown (Informal)

[Tackling Zero Pronoun Resolution and Non-Zero Coreference Resolution Jointly](https://aclanthology.org/2021.conll-1.40) (Chen et al., CoNLL 2021)

• Tackling Zero Pronoun Resolution and Non-Zero Coreference Resolution Jointly (Chen et al., CoNLL 2021)

ACL

• Shisong Chen, Binbin Gu, Jianfeng Qu, Zhixu Li, An Liu, Lei Zhao, and Zhigang Chen. 2021. Tackling Zero Pronoun Resolution and Non-Zero Coreference Resolution Jointly. In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 518–527, Online. Association for Computational Linguistics.