@inproceedings{chai-strube-2022-incorporating,
title = "Incorporating Centering Theory into Neural Coreference Resolution",
author = "Chai, Haixia and
Strube, Michael",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.218",
doi = "10.18653/v1/2022.naacl-main.218",
pages = "2996--3002",
abstract = "In recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset. However, how coreference resolvers can benefit from discourse coherence is still an open question. In this paper, we propose to incorporate centering transitions derived from centering theory in the form of a graph into a neural coreference model. Our method improves the performance over the SOTA baselines, especially on pronoun resolution in long documents, formal well-structured text, and clusters with scattered mentions.",
}
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%0 Conference Proceedings
%T Incorporating Centering Theory into Neural Coreference Resolution
%A Chai, Haixia
%A Strube, Michael
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F chai-strube-2022-incorporating
%X In recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset. However, how coreference resolvers can benefit from discourse coherence is still an open question. In this paper, we propose to incorporate centering transitions derived from centering theory in the form of a graph into a neural coreference model. Our method improves the performance over the SOTA baselines, especially on pronoun resolution in long documents, formal well-structured text, and clusters with scattered mentions.
%R 10.18653/v1/2022.naacl-main.218
%U https://aclanthology.org/2022.naacl-main.218
%U https://doi.org/10.18653/v1/2022.naacl-main.218
%P 2996-3002
Markdown (Informal)
[Incorporating Centering Theory into Neural Coreference Resolution](https://aclanthology.org/2022.naacl-main.218) (Chai & Strube, NAACL 2022)
ACL
- Haixia Chai and Michael Strube. 2022. Incorporating Centering Theory into Neural Coreference Resolution. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2996–3002, Seattle, United States. Association for Computational Linguistics.