@inproceedings{chiril-etal-2021-nice-wife,
title = "{``}Be nice to your wife! The restaurants are closed{''}: Can Gender Stereotype Detection Improve Sexism Classification?",
author = "Chiril, Patricia and
Benamara, Farah and
Moriceau, V{\'e}ronique",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.242",
doi = "10.18653/v1/2021.findings-emnlp.242",
pages = "2833--2844",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chiril-etal-2021-nice-wife">
<titleInfo>
<title>“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification?</title>
</titleInfo>
<name type="personal">
<namePart type="given">Patricia</namePart>
<namePart type="family">Chiril</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Farah</namePart>
<namePart type="family">Benamara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Véronique</namePart>
<namePart type="family">Moriceau</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-nov</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Punta Cana, Dominican Republic</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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>
<identifier type="citekey">chiril-etal-2021-nice-wife</identifier>
<identifier type="doi">10.18653/v1/2021.findings-emnlp.242</identifier>
<location>
<url>https://aclanthology.org/2021.findings-emnlp.242</url>
</location>
<part>
<date>2021-nov</date>
<extent unit="page">
<start>2833</start>
<end>2844</end>
</extent>
</part>
</mods>
</modsCollection>
%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
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 nov
%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)
ACL