<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zou-etal-2024-separately">
    <titleInfo>
        <title>Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xiyuan</namePart>
        <namePart type="family">Zou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yiran</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ian</namePart>
        <namePart type="family">Porada</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jackie</namePart>
        <namePart type="family">Cheung</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="family">Duh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Helena</namePart>
            <namePart type="family">Gomez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Mexico City, Mexico</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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>
    <identifier type="citekey">zou-etal-2024-separately</identifier>
    <location>
        <url>https://aclanthology.org/2024.naacl-short.19</url>
    </location>
    <part>
        <date>2024-06</date>
        <extent unit="page">
            <start>212</start>
            <end>219</end>
        </extent>
    </part>
</mods>
</modsCollection>

%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.
%U https://aclanthology.org/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)

• Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution (Zou et al., NAACL 2024)

ACL

• Xiyuan Zou, Yiran Li, Ian Porada, and Jackie Cheung. 2024. Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 212–219, Mexico City, Mexico. Association for Computational Linguistics.