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        <title>“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification?</title>
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        <namePart type="given">Patricia</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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            <namePart type="given">Marie-Francine</namePart>
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            <namePart type="given">Xuanjing</namePart>
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            <namePart type="given">Lucia</namePart>
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    <abstract>In this paper, we focus on the detection of sexist hate speech against women in tweets studying for the first time the impact of gender stereotype detection on sexism classification. We propose: (1) the first dataset annotated for gender stereotype detection, (2) a new method for data augmentation based on sentence similarity with multilingual external datasets, and (3) a set of deep learning experiments first to detect gender stereotypes and then, to use this auxiliary task for sexism detection. Although the presence of stereotypes does not necessarily entail hateful content, our results show that sexism classification can definitively benefit from gender stereotype detection.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.242</identifier>
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%0 Conference Proceedings
%T “Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification?
%A Chiril, Patricia
%A Benamara, Farah
%A Moriceau, Véronique
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F chiril-etal-2021-nice-wife
%X In this paper, we focus on the detection of sexist hate speech against women in tweets studying for the first time the impact of gender stereotype detection on sexism classification. We propose: (1) the first dataset annotated for gender stereotype detection, (2) a new method for data augmentation based on sentence similarity with multilingual external datasets, and (3) a set of deep learning experiments first to detect gender stereotypes and then, to use this auxiliary task for sexism detection. Although the presence of stereotypes does not necessarily entail hateful content, our results show that sexism classification can definitively benefit from gender stereotype detection.
%R 10.18653/v1/2021.findings-emnlp.242
%U https://aclanthology.org/2021.findings-emnlp.242
%U https://doi.org/10.18653/v1/2021.findings-emnlp.242
%P 2833-2844

Markdown (Informal)

[“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification?](https://aclanthology.org/2021.findings-emnlp.242) (Chiril et al., Findings 2021)

• “Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification? (Chiril et al., Findings 2021)

ACL

• Patricia Chiril, Farah Benamara, and Véronique Moriceau. 2021. “Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification?. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2833–2844, Punta Cana, Dominican Republic. Association for Computational Linguistics.