@inproceedings{zou-etal-2024-separately,
title = "Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution",
author = "Zou, Xiyuan and
Li, Yiran and
Porada, Ian and
Cheung, Jackie",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-short.19",
doi = "10.18653/v1/2024.naacl-short.19",
pages = "212--219",
abstract = "Current end-to-end coreference resolution models combine detection of singleton mentions and antecedent linking into a single step. In contrast, singleton detection was often treated as a separate step in the pre-neural era. In this work, we show that separately parameterizing these two sub-tasks also benefits end-to-end neural coreference systems. Specifically, we add a singleton detector to the coarse-to-fine (C2F) coreference model, and design an anaphoricity-aware span embedding and singleton detection loss. Our method significantly improves model performance on OntoNotes and four additional datasets.",
}
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<abstract>Current end-to-end coreference resolution models combine detection of singleton mentions and antecedent linking into a single step. In contrast, singleton detection was often treated as a separate step in the pre-neural era. In this work, we show that separately parameterizing these two sub-tasks also benefits end-to-end neural coreference systems. Specifically, we add a singleton detector to the coarse-to-fine (C2F) coreference model, and design an anaphoricity-aware span embedding and singleton detection loss. Our method significantly improves model performance on OntoNotes and four additional datasets.</abstract>
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%0 Conference Proceedings
%T Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution
%A Zou, Xiyuan
%A Li, Yiran
%A Porada, Ian
%A Cheung, Jackie
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zou-etal-2024-separately
%X Current end-to-end coreference resolution models combine detection of singleton mentions and antecedent linking into a single step. In contrast, singleton detection was often treated as a separate step in the pre-neural era. In this work, we show that separately parameterizing these two sub-tasks also benefits end-to-end neural coreference systems. Specifically, we add a singleton detector to the coarse-to-fine (C2F) coreference model, and design an anaphoricity-aware span embedding and singleton detection loss. Our method significantly improves model performance on OntoNotes and four additional datasets.
%R 10.18653/v1/2024.naacl-short.19
%U https://aclanthology.org/2024.naacl-short.19
%U https://doi.org/10.18653/v1/2024.naacl-short.19
%P 212-219
Markdown (Informal)
[Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution](https://aclanthology.org/2024.naacl-short.19) (Zou et al., NAACL 2024)
ACL