NAACL 2021long9 citations
Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance
Sopan Khosla, James Fiacco, Carolyn Rosé
Abstract
Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this work, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference-resolution datasets. We explore how the impact varies depending upon the type of mention.
BibTeX
@inproceedings{khosla-etal-2021-evaluating,
title = "Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance",
author = "Khosla, Sopan and
Fiacco, James and
Ros{\'e}, Carolyn",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.130/",
doi = "10.18653/v1/2021.naacl-main.130",
pages = "1645--1651"
}