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
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.findings-emnlp.242.pdf
- Data
- ConceptNet