Methods for detoxification of texts for the russian language

Daryna Dementieva, Daniil Moskovskiy, Varvara Logacheva, David Dale, Olga Kozlova, Nikita Semenov, Alexander Panchenko

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


We introduce the first study of the automatic detoxification of Russian texts to combat offensive language. This kind of textual style transfer can be used for processing toxic content on social media or for eliminating toxicity in automatically generated texts. While much work has been done for the English language in this field, there are no works on detoxification for the Russian language. We suggest two types of models—an approach based on BERT architecture that performs local corrections and a supervised approach based on a pretrained GPT-2 language model. We compare these methods with several baselines. In addition, we provide the training datasets and describe the evaluation setup and metrics for automatic and manual evaluation. The results show that the tested approaches can be successfully used for detoxification, although there is room for improvement.

Original languageEnglish
Article number54
JournalMultimodal Technologies and Interaction
Issue number9
Publication statusPublished - Sep 2021


  • Detoxification
  • Pretrained models
  • Text style transfer
  • Toxicity detection


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