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        <title>How Does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?</title>
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        <namePart type="given">Hila</namePart>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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    <abstract>Many natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun’s gender. We show that this affects the word representations of inanimate nouns, resulting in nouns with the same gender being closer to each other than nouns with different gender. While “embedding debiasing” methods fail to remove the effect, we demonstrate that a careful application of methods that neutralize grammatical gender signals from the words’ context when training word embeddings is effective in removing it. Fixing the grammatical gender bias yields a positive effect on the quality of the resulting word embeddings, both in monolingual and cross-lingual settings. We note that successfully removing gender signals, while achievable, is not trivial to do and that a language-specific morphological analyzer, together with careful usage of it, are essential for achieving good results.</abstract>
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    <identifier type="doi">10.18653/v1/K19-1043</identifier>
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%0 Conference Proceedings
%T How Does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?
%A Gonen, Hila
%A Kementchedjhieva, Yova
%A Goldberg, Yoav
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F gonen-etal-2019-grammatical
%X Many natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun’s gender. We show that this affects the word representations of inanimate nouns, resulting in nouns with the same gender being closer to each other than nouns with different gender. While “embedding debiasing” methods fail to remove the effect, we demonstrate that a careful application of methods that neutralize grammatical gender signals from the words’ context when training word embeddings is effective in removing it. Fixing the grammatical gender bias yields a positive effect on the quality of the resulting word embeddings, both in monolingual and cross-lingual settings. We note that successfully removing gender signals, while achievable, is not trivial to do and that a language-specific morphological analyzer, together with careful usage of it, are essential for achieving good results.
%R 10.18653/v1/K19-1043
%U https://aclanthology.org/K19-1043
%U https://doi.org/10.18653/v1/K19-1043
%P 463-471

Markdown (Informal)

[How Does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?](https://aclanthology.org/K19-1043) (Gonen et al., CoNLL 2019)

• How Does Grammatical Gender Affect Noun Representations in Gender-Marking Languages? (Gonen et al., CoNLL 2019)

ACL

• Hila Gonen, Yova Kementchedjhieva, and Yoav Goldberg. 2019. How Does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 463–471, Hong Kong, China. Association for Computational Linguistics.