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        <title>How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?</title>
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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 results in 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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%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 Axelrod, Amittai
%Y Yang, Diyi
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Waseem, Zeerak
%S Proceedings of the 2019 Workshop on Widening NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F gonen-etal-2019-grammatical-gender
%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 results in 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.
%U https://aclanthology.org/W19-3622
%P 64-67

Markdown (Informal)

[How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?](https://aclanthology.org/W19-3622) (Gonen et al., WiNLP 2019)

• How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages? (Gonen et al., WiNLP 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 2019 Workshop on Widening NLP, pages 64–67, Florence, Italy. Association for Computational Linguistics.