@inproceedings{kobayashi-etal-2022-constrained,
title = "Constrained Multi-Task Learning for Bridging Resolution",
author = "Kobayashi, Hideo and
Hou, Yufang and
Ng, Vincent",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.56",
doi = "10.18653/v1/2022.acl-long.56",
pages = "759--770",
abstract = "We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-train the entity coreference model in the multi-task framework on the large amount of publicly available coreference data; and (3) integrating prior knowledge encoded in rule-based resolvers. Our approach achieves state-of-the-art results on three standard evaluation corpora.",
}
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<abstract>We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-train the entity coreference model in the multi-task framework on the large amount of publicly available coreference data; and (3) integrating prior knowledge encoded in rule-based resolvers. Our approach achieves state-of-the-art results on three standard evaluation corpora.</abstract>
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%0 Conference Proceedings
%T Constrained Multi-Task Learning for Bridging Resolution
%A Kobayashi, Hideo
%A Hou, Yufang
%A Ng, Vincent
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kobayashi-etal-2022-constrained
%X We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-train the entity coreference model in the multi-task framework on the large amount of publicly available coreference data; and (3) integrating prior knowledge encoded in rule-based resolvers. Our approach achieves state-of-the-art results on three standard evaluation corpora.
%R 10.18653/v1/2022.acl-long.56
%U https://aclanthology.org/2022.acl-long.56
%U https://doi.org/10.18653/v1/2022.acl-long.56
%P 759-770
Markdown (Informal)
[Constrained Multi-Task Learning for Bridging Resolution](https://aclanthology.org/2022.acl-long.56) (Kobayashi et al., ACL 2022)
ACL
- Hideo Kobayashi, Yufang Hou, and Vincent Ng. 2022. Constrained Multi-Task Learning for Bridging Resolution. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 759–770, Dublin, Ireland. Association for Computational Linguistics.