“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification?

Patricia Chiril, Farah Benamara, Véronique Moriceau


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.
Anthology ID:
2021.findings-emnlp.242
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2833–2844
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.242
DOI:
10.18653/v1/2021.findings-emnlp.242
Bibkey:
Cite (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.
Cite (Informal):
“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification? (Chiril et al., Findings 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2021.findings-emnlp.242.pdf
Data
ConceptNet