<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="reusens-etal-2023-investigating">
    <titleInfo>
        <title>Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Manon</namePart>
        <namePart type="family">Reusens</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Philipp</namePart>
        <namePart type="family">Borchert</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Margot</namePart>
        <namePart type="family">Mieskes</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jochen</namePart>
        <namePart type="family">De Weerdt</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Bart</namePart>
        <namePart type="family">Baesens</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</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>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper investigates the transferability of debiasing techniques across different languages within multilingual models. We examine the applicability of these techniques in English, French, German, and Dutch. Using multilingual BERT (mBERT), we demonstrate that cross-lingual transfer of debiasing techniques is not only feasible but also yields promising results. Surprisingly, our findings reveal no performance disadvantages when applying these techniques to non-English languages. Using translations of the CrowS-Pairs dataset, our analysis identifies SentenceDebias as the best technique across different languages, reducing bias in mBERT by an average of 13%. We also find that debiasing techniques with additional pretraining exhibit enhanced cross-lingual effectiveness for the languages included in the analyses, particularly in lower-resource languages. These novel insights contribute to a deeper understanding of bias mitigation in multilingual language models and provide practical guidance for debiasing techniques in different language contexts.</abstract>
    <identifier type="citekey">reusens-etal-2023-investigating</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-main.175</identifier>
    <location>
        <url>https://aclanthology.org/2023.emnlp-main.175</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>2887</start>
            <end>2896</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques
%A Reusens, Manon
%A Borchert, Philipp
%A Mieskes, Margot
%A De Weerdt, Jochen
%A Baesens, Bart
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F reusens-etal-2023-investigating
%X This paper investigates the transferability of debiasing techniques across different languages within multilingual models. We examine the applicability of these techniques in English, French, German, and Dutch. Using multilingual BERT (mBERT), we demonstrate that cross-lingual transfer of debiasing techniques is not only feasible but also yields promising results. Surprisingly, our findings reveal no performance disadvantages when applying these techniques to non-English languages. Using translations of the CrowS-Pairs dataset, our analysis identifies SentenceDebias as the best technique across different languages, reducing bias in mBERT by an average of 13%. We also find that debiasing techniques with additional pretraining exhibit enhanced cross-lingual effectiveness for the languages included in the analyses, particularly in lower-resource languages. These novel insights contribute to a deeper understanding of bias mitigation in multilingual language models and provide practical guidance for debiasing techniques in different language contexts.
%R 10.18653/v1/2023.emnlp-main.175
%U https://aclanthology.org/2023.emnlp-main.175
%U https://doi.org/10.18653/v1/2023.emnlp-main.175
%P 2887-2896

Markdown (Informal)

[Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques](https://aclanthology.org/2023.emnlp-main.175) (Reusens et al., EMNLP 2023)

• Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques (Reusens et al., EMNLP 2023)

ACL

• Manon Reusens, Philipp Borchert, Margot Mieskes, Jochen De Weerdt, and Bart Baesens. 2023. Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2887–2896, Singapore. Association for Computational Linguistics.