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        <title>Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification</title>
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        <namePart type="given">Xiaolei</namePart>
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            <title>Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies</title>
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            <namePart type="given">Marine</namePart>
            <namePart type="family">Carpuat</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Marie-Catherine</namePart>
            <namePart type="family">de Marneffe</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ivan</namePart>
            <namePart type="given">Vladimir</namePart>
            <namePart type="family">Meza Ruiz</namePart>
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    <abstract>Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined. In this work, we treat the gender as domains (e.g., male vs. female) and present a standard domain adaptation model to reduce the gender bias and improve performance of text classifiers under multilingual settings. We evaluate our approach on two text classification tasks, hate speech detection and rating prediction, and demonstrate the effectiveness of our approach with three fair-aware baselines.</abstract>
    <identifier type="citekey">huang-2022-easy</identifier>
    <identifier type="doi">10.18653/v1/2022.naacl-main.52</identifier>
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%0 Conference Proceedings
%T Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification
%A Huang, Xiaolei
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F huang-2022-easy
%X Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined. In this work, we treat the gender as domains (e.g., male vs. female) and present a standard domain adaptation model to reduce the gender bias and improve performance of text classifiers under multilingual settings. We evaluate our approach on two text classification tasks, hate speech detection and rating prediction, and demonstrate the effectiveness of our approach with three fair-aware baselines.
%R 10.18653/v1/2022.naacl-main.52
%U https://aclanthology.org/2022.naacl-main.52
%U https://doi.org/10.18653/v1/2022.naacl-main.52
%P 717-723

Markdown (Informal)

[Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification](https://aclanthology.org/2022.naacl-main.52) (Huang, NAACL 2022)

• Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification (Huang, NAACL 2022)

ACL

• Xiaolei Huang. 2022. Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 717–723, Seattle, United States. Association for Computational Linguistics.