<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="triboulet-bouillon-2023-evaluating">
    <titleInfo>
        <title>Evaluating the Impact of Stereotypes and Language Combinations on Gender Bias Occurrence in NMT Generic Systems</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Bertille</namePart>
        <namePart type="family">Triboulet</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Pierrette</namePart>
        <namePart type="family">Bouillon</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Bharathi</namePart>
            <namePart type="given">R</namePart>
            <namePart type="family">Chakravarthi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">B</namePart>
            <namePart type="family">Bharathi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joephine</namePart>
            <namePart type="family">Griffith</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Paul</namePart>
            <namePart type="family">Buitelaar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Machine translation, and more specifically neural machine translation (NMT), have been proven to be subject to gender bias in recent years. Many studies have focused on evaluating and reducing this phenomenon, mainly through the analysis of occupational nouns’ translation for the same type of language combinations. In this paper, we reproduce a similar test set than in previous studies to investigate the influence of stereotypes and language combinations’ nature (formed with English, French and Italian) on gender bias occurrence in NMT. Similarly to previous studies, we confirm stereotypes as a major source of gender bias, especially in female contexts, while observing bias even in language combinations traditionally less examined.</abstract>
    <identifier type="citekey">triboulet-bouillon-2023-evaluating</identifier>
    <location>
        <url>https://aclanthology.org/2023.ltedi-1.9</url>
    </location>
    <part>
        <date>2023-09</date>
        <extent unit="page">
            <start>62</start>
            <end>70</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Evaluating the Impact of Stereotypes and Language Combinations on Gender Bias Occurrence in NMT Generic Systems
%A Triboulet, Bertille
%A Bouillon, Pierrette
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F triboulet-bouillon-2023-evaluating
%X Machine translation, and more specifically neural machine translation (NMT), have been proven to be subject to gender bias in recent years. Many studies have focused on evaluating and reducing this phenomenon, mainly through the analysis of occupational nouns’ translation for the same type of language combinations. In this paper, we reproduce a similar test set than in previous studies to investigate the influence of stereotypes and language combinations’ nature (formed with English, French and Italian) on gender bias occurrence in NMT. Similarly to previous studies, we confirm stereotypes as a major source of gender bias, especially in female contexts, while observing bias even in language combinations traditionally less examined.
%U https://aclanthology.org/2023.ltedi-1.9
%P 62-70

Markdown (Informal)

[Evaluating the Impact of Stereotypes and Language Combinations on Gender Bias Occurrence in NMT Generic Systems](https://aclanthology.org/2023.ltedi-1.9) (Triboulet & Bouillon, LTEDI-WS 2023)

• Evaluating the Impact of Stereotypes and Language Combinations on Gender Bias Occurrence in NMT Generic Systems (Triboulet & Bouillon, LTEDI-WS 2023)

ACL

• Bertille Triboulet and Pierrette Bouillon. 2023. Evaluating the Impact of Stereotypes and Language Combinations on Gender Bias Occurrence in NMT Generic Systems. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 62–70, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.