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

Patricia Chiril; Farah Benamara; Véronique Moriceau

EMNLP 2021finding29 citations

“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.

BibTeX

@inproceedings{chiril-etal-2021-nice-wife,
    title = "{\textquotedblleft}Be nice to your wife! The restaurants are closed{\textquotedblright}: Can Gender Stereotype Detection Improve Sexism Classification?",
    author = "Chiril, Patricia  and
      Benamara, Farah  and
      Moriceau, V{\'e}ronique",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    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"
}