<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2021-brief">
    <titleInfo>
        <title>A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hongming</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xinran</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yangqiu</namePart>
        <namePart type="family">Song</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Punta Cana, Dominican Republic</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to. Compared with the general coreference resolution task, the main challenge of PCR is the coreference relation prediction rather than the mention detection. As one important natural language understanding (NLU) component, pronoun resolution is crucial for many downstream tasks and still challenging for existing models, which motivates us to survey existing approaches and think about how to do better. In this survey, we first introduce representative datasets and models for the ordinary pronoun coreference resolution task. Then we focus on recent progress on hard pronoun coreference resolution problems (e.g., Winograd Schema Challenge) to analyze how well current models can understand commonsense. We conduct extensive experiments to show that even though current models are achieving good performance on the standard evaluation set, they are still not ready to be used in real applications (e.g., all SOTA models struggle on correctly resolving pronouns to infrequent objects). All experiment codes will be available upon acceptance.</abstract>
    <identifier type="citekey">zhang-etal-2021-brief</identifier>
    <identifier type="doi">10.18653/v1/2021.crac-1.1</identifier>
    <location>
        <url>https://aclanthology.org/2021.crac-1.1</url>
    </location>
    <part>
        <date>2021-11</date>
        <extent unit="page">
            <start>1</start>
            <end>11</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English
%A Zhang, Hongming
%A Zhao, Xinran
%A Song, Yangqiu
%S Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F zhang-etal-2021-brief
%X Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to. Compared with the general coreference resolution task, the main challenge of PCR is the coreference relation prediction rather than the mention detection. As one important natural language understanding (NLU) component, pronoun resolution is crucial for many downstream tasks and still challenging for existing models, which motivates us to survey existing approaches and think about how to do better. In this survey, we first introduce representative datasets and models for the ordinary pronoun coreference resolution task. Then we focus on recent progress on hard pronoun coreference resolution problems (e.g., Winograd Schema Challenge) to analyze how well current models can understand commonsense. We conduct extensive experiments to show that even though current models are achieving good performance on the standard evaluation set, they are still not ready to be used in real applications (e.g., all SOTA models struggle on correctly resolving pronouns to infrequent objects). All experiment codes will be available upon acceptance.
%R 10.18653/v1/2021.crac-1.1
%U https://aclanthology.org/2021.crac-1.1
%U https://doi.org/10.18653/v1/2021.crac-1.1
%P 1-11

Markdown (Informal)

[A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English](https://aclanthology.org/2021.crac-1.1) (Zhang et al., CRAC 2021)

• A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English (Zhang et al., CRAC 2021)

ACL

• Hongming Zhang, Xinran Zhao, and Yangqiu Song. 2021. A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English. In Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference, pages 1–11, Punta Cana, Dominican Republic. Association for Computational Linguistics.