Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection

Debora Nozza


Abstract
Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted exclusively on English and very few consistently labeled, non-English resources have been proposed. Learning to detect hate speech on English and transferring to unseen languages seems an immediate solution. This work is the first to shed light on the limits of this zero-shot, cross-lingual transfer learning framework for hate speech detection. We use benchmark data sets in English, Italian, and Spanish to detect hate speech towards immigrants and women. Investigating post-hoc explanations of the model, we discover that non-hateful, language-specific taboo interjections are misinterpreted as signals of hate speech. Our findings demonstrate that zero-shot, cross-lingual models cannot be used as they are, but need to be carefully designed.
Anthology ID:
2021.acl-short.114
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
907–914
Language:
URL:
https://aclanthology.org/2021.acl-short.114
DOI:
10.18653/v1/2021.acl-short.114
Bibkey:
Cite (ACL):
Debora Nozza. 2021. Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 907–914, Online. Association for Computational Linguistics.
Cite (Informal):
Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection (Nozza, ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-short.114.pdf
Video:
 https://aclanthology.org/2021.acl-short.114.mp4
Data
HatEval