  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) 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.
