@inproceedings{yuan-etal-2022-adapting,
title = "Adapting Coreference Resolution Models through Active Learning",
author = "Yuan, Michelle and
Xia, Patrick and
May, Chandler and
Van Durme, Benjamin and
Boyd-Graber, Jordan",
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.519",
doi = "10.18653/v1/2022.acl-long.519",
pages = "7533--7549",
abstract = "Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.",
}
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<abstract>Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.</abstract>
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%0 Conference Proceedings
%T Adapting Coreference Resolution Models through Active Learning
%A Yuan, Michelle
%A Xia, Patrick
%A May, Chandler
%A Van Durme, Benjamin
%A Boyd-Graber, Jordan
%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 yuan-etal-2022-adapting
%X Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference resolution is neither well-defined nor fully understood. This paper explores how to actively label coreference, examining sources of model uncertainty and document reading costs. We compare uncertainty sampling strategies and their advantages through thorough error analysis. In both synthetic and human experiments, labeling spans within the same document is more effective than annotating spans across documents. The findings contribute to a more realistic development of coreference resolution models.
%R 10.18653/v1/2022.acl-long.519
%U https://aclanthology.org/2022.acl-long.519
%U https://doi.org/10.18653/v1/2022.acl-long.519
%P 7533-7549
Markdown (Informal)
[Adapting Coreference Resolution Models through Active Learning](https://aclanthology.org/2022.acl-long.519) (Yuan et al., ACL 2022)
ACL
- Michelle Yuan, Patrick Xia, Chandler May, Benjamin Van Durme, and Jordan Boyd-Graber. 2022. Adapting Coreference Resolution Models through Active Learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7533–7549, Dublin, Ireland. Association for Computational Linguistics.