mlsanityData to 7 Oct 2026

Methods for Detoxification of Texts for the Russian Language

arXiv 2105.09052 ↗First posted 19 May 2021cs.CL cs.LGcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 24 of its 46 references that are in the map. First posted on arXiv 19 May 2021.

Builds on

24 of 46 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  4. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
  5. 2019Predicting the Type and Target of Offensive Posts in Social Mediacited by 268
  6. 2017Style Transfer from Non-Parallel Text by Cross-Alignmentcited by 246
  7. 2018Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfercited by 209
  8. 2018Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfercited by 191
  9. 2020Reformulating Unsupervised Style Transfer as Paraphrase Generationcited by 181
  10. 2020Deep Learning for Text Style Transfer: A Surveycited by 161
  11. 2018Disentangled Representation Learning for Non-Parallel Text Style Transfercited by 127
  12. 2019Adaptation of Deep Bidirectional Multilingual Transformers for Russian Languagecited by 109
12 more
  1. 2018Conditional BERT Contextual Augmentationcited by 107
  2. 2019Automatically Neutralizing Subjective Bias in Textcited by 105
  3. 2020Politeness Transfer: A Tag and Generate Approachcited by 78
  4. 2018Fighting Offensive Language on Social Media with Unsupervised Text Style Transfercited by 55
  5. 2019"Mask and Infill" : Applying Masked Language Model to Sentiment Transfercited by 48
  6. 2020Load What You Need: Smaller Versions of Multilingual BERTcited by 32
  7. 2020Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metriccited by 23
  8. 2020Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redactioncited by 17
  9. 2019Towards non-toxic landscapes: Automatic toxic comment detection using DNNcited by 8
  10. 2020Fortifying Toxic Speech Detectors Against Veiled Toxicitycited by 7
  11. 2018What is wrong with style transfer for texts?cited by 4
  12. 2017Improved Neural Text Attribute Transfer with Non-parallel Datacited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 5; 2025: 3; 2026: 2.

13 papers.

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