@inproceedings{song-etal-2020-zpr2,
title = "{ZPR}2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and {BERT}",
author = "Song, Linfeng and
Xu, Kun and
Zhang, Yue and
Chen, Jianshu and
Yu, Dong",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.482/",
doi = "10.18653/v1/2020.acl-main.482",
pages = "5429--5434",
abstract = "Zero pronoun recovery and resolution aim at recovering the dropped pronoun and pointing out its anaphoric mentions, respectively. We propose to better explore their interaction by solving both tasks together, while the previous work treats them separately. For zero pronoun resolution, we study this task in a more realistic setting, where no parsing trees or only automatic trees are available, while most previous work assumes gold trees. Experiments on two benchmarks show that joint modeling significantly outperforms our baseline that already beats the previous state of the arts."
}
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<abstract>Zero pronoun recovery and resolution aim at recovering the dropped pronoun and pointing out its anaphoric mentions, respectively. We propose to better explore their interaction by solving both tasks together, while the previous work treats them separately. For zero pronoun resolution, we study this task in a more realistic setting, where no parsing trees or only automatic trees are available, while most previous work assumes gold trees. Experiments on two benchmarks show that joint modeling significantly outperforms our baseline that already beats the previous state of the arts.</abstract>
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%0 Conference Proceedings
%T ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT
%A Song, Linfeng
%A Xu, Kun
%A Zhang, Yue
%A Chen, Jianshu
%A Yu, Dong
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F song-etal-2020-zpr2
%X Zero pronoun recovery and resolution aim at recovering the dropped pronoun and pointing out its anaphoric mentions, respectively. We propose to better explore their interaction by solving both tasks together, while the previous work treats them separately. For zero pronoun resolution, we study this task in a more realistic setting, where no parsing trees or only automatic trees are available, while most previous work assumes gold trees. Experiments on two benchmarks show that joint modeling significantly outperforms our baseline that already beats the previous state of the arts.
%R 10.18653/v1/2020.acl-main.482
%U https://aclanthology.org/2020.acl-main.482/
%U https://doi.org/10.18653/v1/2020.acl-main.482
%P 5429-5434
Markdown (Informal)
[ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT](https://aclanthology.org/2020.acl-main.482/) (Song et al., ACL 2020)
ACL