  Text detoxification is a style transfer task of creating neutral versions of
toxic texts. In this paper, we use the concept of text editing to build a
two-step tagging-based detoxification model using a parallel corpus of Russian
texts. With this model, we achieved the best style transfer accuracy among all
models in the RUSSE Detox shared task, surpassing larger sequence-to-sequence
models.
