<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="go-falenska-2024-gender">
    <titleInfo>
        <title>Is there Gender Bias in Dependency Parsing? Revisiting “Women’s Syntactic Resilience”</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Paul</namePart>
        <namePart type="family">Go</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Agnieszka</namePart>
        <namePart type="family">Falenska</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Agnieszka</namePart>
            <namePart type="family">Faleńska</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christine</namePart>
            <namePart type="family">Basta</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marta</namePart>
            <namePart type="family">Costa-jussà</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Seraphina</namePart>
            <namePart type="family">Goldfarb-Tarrant</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Debora</namePart>
            <namePart type="family">Nozza</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Bangkok, Thailand</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we revisit the seminal work of Garimella et al. 2019, who reported that dependency parsers learn demographically-related signals from their training data and perform differently on sentences authored by people of different genders. We re-run all the parsing experiments from Garimella et al. 2019 and find that their results are not reproducible. Additionally, the original patterns suggesting the presence of gender biases fail to generalize to other treebank and parsing architecture. Instead, our data analysis uncovers methodological shortcomings in the initial study that artificially introduced differences into female and male datasets during preprocessing. These disparities potentially compromised the validity of the original conclusions.</abstract>
    <identifier type="citekey">go-falenska-2024-gender</identifier>
    <location>
        <url>https://aclanthology.org/2024.gebnlp-1.17</url>
    </location>
    <part>
        <date>2024-08</date>
        <extent unit="page">
            <start>269</start>
            <end>279</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Is there Gender Bias in Dependency Parsing? Revisiting “Women’s Syntactic Resilience”
%A Go, Paul
%A Falenska, Agnieszka
%Y Faleńska, Agnieszka
%Y Basta, Christine
%Y Costa-jussà, Marta
%Y Goldfarb-Tarrant, Seraphina
%Y Nozza, Debora
%S Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F go-falenska-2024-gender
%X In this paper, we revisit the seminal work of Garimella et al. 2019, who reported that dependency parsers learn demographically-related signals from their training data and perform differently on sentences authored by people of different genders. We re-run all the parsing experiments from Garimella et al. 2019 and find that their results are not reproducible. Additionally, the original patterns suggesting the presence of gender biases fail to generalize to other treebank and parsing architecture. Instead, our data analysis uncovers methodological shortcomings in the initial study that artificially introduced differences into female and male datasets during preprocessing. These disparities potentially compromised the validity of the original conclusions.
%U https://aclanthology.org/2024.gebnlp-1.17
%P 269-279

Markdown (Informal)

[Is there Gender Bias in Dependency Parsing? Revisiting “Women’s Syntactic Resilience”](https://aclanthology.org/2024.gebnlp-1.17) (Go & Falenska, GeBNLP-WS 2024)

• Is there Gender Bias in Dependency Parsing? Revisiting “Women’s Syntactic Resilience” (Go & Falenska, GeBNLP-WS 2024)

ACL

• Paul Go and Agnieszka Falenska. 2024. Is there Gender Bias in Dependency Parsing? Revisiting “Women’s Syntactic Resilience”. In Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 269–279, Bangkok, Thailand. Association for Computational Linguistics.