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Multilingual and Explainable Text Detoxification with Parallel Corpora

Summary

Online abusive language remains a problem despite regulation. One remedy is text detoxification, which rewrites offensive text into a neutral version while keeping its meaning. The best systems rely on parallel toxic–neutral pairs, but such data existed for only a few mostly European languages. The authors manually built ParaDetox-style parallel corpora for German, Chinese, Arabic, Hindi and Amharic. Combined with existing English, Russian, Ukrainian and Spanish data, this gives nine languages. Toxicity was limited to vulgar or profane wording rather than deep insults or hate. Using LLMs as explainability tools, they analysed what characterises toxic and detoxified sentences in each language, which toxic collocations dominate, and which edits (such as deletion versus rephrasing) are needed. These insights fed a Chain-of-Thoughts prompting method for GPT-4 that adds information about detoxification clusters. Among the tested approaches, this method gave the strongest style-transfer accuracy and the best average joint score. The resources support detoxification research beyond high-resource European languages.

Key points

  • New parallel data per language (toxic candidates → detoxified pairs): German 3 521 → 1 103; Hindi 2 328 → 1 007; Amharic 2 995 → 1 000; Arabic 2 100 → 1 181; Chinese 1 380 → 1 000.
  • German sources include GermEval 2018 and GermEval 2021 plus a hate-speech set by Ross et al. (2016); Hindi draws on HASOC (FIRE 2019). Annotation took about four months, with paid native-speaker annotators.
  • Baselines included Delete, mBART-Large fine-tuned on human-curated versus translated data, and GPT-4 few-shot and CoT prompting. Delete was the best unsupervised method for Chinese, Arabic and Amharic.
  • Human-curated fine-tuning data usually beat translated data, notably for German, Hindi, Ukrainian and Amharic, where cultural nuances matter.
  • Data and code are on Hugging Face (textdetox) and GitHub, and they formed the basis of the TextDetox CLEF-2024 Shared Task.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Machine translation · Natural language processing

Cited by

18 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 17 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{dementieva2025multilingual,
  title = {{Multilingual and Explainable Text Detoxification with Parallel Corpora}},
  author = {Dementieva, Daryna and Babakov, Nikolay and Ronen, Amit and Ayele, Abinew Ali and Rizwan, Naquee and Schneider, Florian and Wang, Xintong and Yimam, Seid Muhie and Moskovskiy, Daniil and Stakovskii, Elisei and Kaufman, Eran and Elnagar, Ashraf and Mukherjee, Animesh and Panchenko, Alexander},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.535/}
}

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