NAACL 2024short0 citations
Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution
Xiyuan Zou, Yiran Li, Ian Porada, Jackie Cheung
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.
BibTeX
@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"
}