@inproceedings{chiril-etal-2020-said,
title = "He said {``}who{'}s gonna take care of your children when you are at {ACL}?{''}: Reported Sexist Acts are Not Sexist",
author = "Chiril, Patricia and
Moriceau, V{\'e}ronique and
Benamara, Farah and
Mari, Alda and
Origgi, Gloria and
Coulomb-Gully, Marl{\`e}ne",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.373",
doi = "10.18653/v1/2020.acl-main.373",
pages = "4055--4066",
abstract = "In a context of offensive content mediation on social media now regulated by European laws, it is important not only to be able to automatically detect sexist content but also to identify if a message with a sexist content is really sexist or is a story of sexism experienced by a woman. We propose: (1) a new characterization of sexist content inspired by speech acts theory and discourse analysis studies, (2) the first French dataset annotated for sexism detection, and (3) a set of deep learning experiments trained on top of a combination of several tweet{'}s vectorial representations (word embeddings, linguistic features, and various generalization strategies). Our results are encouraging and constitute a first step towards offensive content moderation.",
}
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<abstract>In a context of offensive content mediation on social media now regulated by European laws, it is important not only to be able to automatically detect sexist content but also to identify if a message with a sexist content is really sexist or is a story of sexism experienced by a woman. We propose: (1) a new characterization of sexist content inspired by speech acts theory and discourse analysis studies, (2) the first French dataset annotated for sexism detection, and (3) a set of deep learning experiments trained on top of a combination of several tweet’s vectorial representations (word embeddings, linguistic features, and various generalization strategies). Our results are encouraging and constitute a first step towards offensive content moderation.</abstract>
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%0 Conference Proceedings
%T He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist
%A Chiril, Patricia
%A Moriceau, Véronique
%A Benamara, Farah
%A Mari, Alda
%A Origgi, Gloria
%A Coulomb-Gully, Marlène
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F chiril-etal-2020-said
%X In a context of offensive content mediation on social media now regulated by European laws, it is important not only to be able to automatically detect sexist content but also to identify if a message with a sexist content is really sexist or is a story of sexism experienced by a woman. We propose: (1) a new characterization of sexist content inspired by speech acts theory and discourse analysis studies, (2) the first French dataset annotated for sexism detection, and (3) a set of deep learning experiments trained on top of a combination of several tweet’s vectorial representations (word embeddings, linguistic features, and various generalization strategies). Our results are encouraging and constitute a first step towards offensive content moderation.
%R 10.18653/v1/2020.acl-main.373
%U https://aclanthology.org/2020.acl-main.373
%U https://doi.org/10.18653/v1/2020.acl-main.373
%P 4055-4066
Markdown (Informal)
[He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist](https://aclanthology.org/2020.acl-main.373) (Chiril et al., ACL 2020)
ACL