Reducing Unintended Identity Bias in Russian Hate Speech Detection

Nadezhda Zueva, Madina Kabirova, Pavel Kalaidin


Abstract
Toxicity has become a grave problem for many online communities, and has been growing across many languages, including Russian. Hate speech creates an environment of intimidation, discrimination, and may even incite some real-world violence. Both researchers and social platforms have been focused on developing models to detect toxicity in online communication for a while now. A common problem of these models is the presence of bias towards some words (e.g. woman, black, jew or женщина, черный, еврей) that are not toxic, but serve as triggers for the classifier due to model caveats. In this paper, we describe our efforts towards classifying hate speech in Russian, and propose simple techniques of reducing unintended bias, such as generating training data with language models using terms and words related to protected identities as context and applying word dropout to such words.
Anthology ID:
2020.alw-1.8
Volume:
Proceedings of the Fourth Workshop on Online Abuse and Harms
Month:
November
Year:
2020
Address:
Online
Editors:
Seyi Akiwowo, Bertie Vidgen, Vinodkumar Prabhakaran, Zeerak Waseem
Venue:
ALW
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
65–69
Language:
URL:
https://aclanthology.org/2020.alw-1.8
DOI:
10.18653/v1/2020.alw-1.8
Bibkey:
Cite (ACL):
Nadezhda Zueva, Madina Kabirova, and Pavel Kalaidin. 2020. Reducing Unintended Identity Bias in Russian Hate Speech Detection. In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 65–69, Online. Association for Computational Linguistics.
Cite (Informal):
Reducing Unintended Identity Bias in Russian Hate Speech Detection (Zueva et al., ALW 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.alw-1.8.pdf
Video:
 https://slideslive.com/38939524