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    <titleInfo>
        <title>On the Generalization of Projection-Based Gender Debiasing in Word Embedding</title>
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    <name type="personal">
        <namePart type="given">Elisabetta</namePart>
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    <name type="personal">
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            <title>Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Gender bias estimation and mitigation techniques in word embeddings lack an understanding of their generalization capabilities. In this work, we complement prior research by comparing in a systematic way four gender bias metrics (Word Embedding Association Tes, Relative Negative Sentiment Bias, Embedding Coherence Test and Bias Analogy Test), two types of projection-based gender mitigation strategies (hard- and soft-debiasing) on three well-known word embedding representations (Word2Vec, FastText and Glove). The experiments have shown that the considered word embeddings are consistent between them but the debiasing techniques are inconsistent across the different metrics, also highlighting the potential risk of unintended bias after the mitigation strategies.</abstract>
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%0 Conference Proceedings
%T On the Generalization of Projection-Based Gender Debiasing in Word Embedding
%A Fersini, Elisabetta
%A Candelieri, Antonio
%A Pastore, Lorenzo
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F fersini-etal-2023-generalization
%X Gender bias estimation and mitigation techniques in word embeddings lack an understanding of their generalization capabilities. In this work, we complement prior research by comparing in a systematic way four gender bias metrics (Word Embedding Association Tes, Relative Negative Sentiment Bias, Embedding Coherence Test and Bias Analogy Test), two types of projection-based gender mitigation strategies (hard- and soft-debiasing) on three well-known word embedding representations (Word2Vec, FastText and Glove). The experiments have shown that the considered word embeddings are consistent between them but the debiasing techniques are inconsistent across the different metrics, also highlighting the potential risk of unintended bias after the mitigation strategies.
%U https://aclanthology.org/2023.ranlp-1.38
%P 336-343

Markdown (Informal)

[On the Generalization of Projection-Based Gender Debiasing in Word Embedding](https://aclanthology.org/2023.ranlp-1.38) (Fersini et al., RANLP 2023)

• On the Generalization of Projection-Based Gender Debiasing in Word Embedding (Fersini et al., RANLP 2023)

ACL

• Elisabetta Fersini, Antonio Candelieri, and Lorenzo Pastore. 2023. On the Generalization of Projection-Based Gender Debiasing in Word Embedding. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 336–343, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.