@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",
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.",
}
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%0 Conference Proceedings
%T Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance
%A Khosla, Sopan
%A Fiacco, James
%A Rosé, Carolyn
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F khosla-etal-2021-evaluating
%X 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.
%R 10.18653/v1/2021.naacl-main.130
%U https://aclanthology.org/2021.naacl-main.130
%U https://doi.org/10.18653/v1/2021.naacl-main.130
%P 1645-1651
Markdown (Informal)
[Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance](https://aclanthology.org/2021.naacl-main.130) (Khosla et al., NAACL 2021)
ACL