  We introduce the first study of automatic detoxification of Russian texts to
combat offensive language. Such a kind of textual style transfer can be used,
for instance, for processing toxic content in social media. While much work has
been done for the English language in this field, it has never been solved for
the Russian language yet. We test two types of models - unsupervised approach
based on BERT architecture that performs local corrections and supervised
approach based on pretrained language GPT-2 model - and compare them with
several baselines. In addition, we describe evaluation setup providing training
datasets and metrics for automatic evaluation. The results show that the tested
approaches can be successfully used for detoxification, although there is room
for improvement.
