@inproceedings{moskovskiy-etal-2022-exploring,
    title = "Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models.",
    author = "Moskovskiy, Daniil  and
      Dementieva, Daryna  and
      Panchenko, Alexander",
    editor = "Louvan, Samuel  and
      Madotto, Andrea  and
      Madureira, Brielen",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.acl-srw.26/",
    doi = "10.18653/v1/2022.acl-srw.26",
    pages = "346--354",
    abstract = "Detoxification is a task of generating text in polite style while preserving meaning and fluency of the original toxic text. Existing detoxification methods are monolingual i.e. designed to work in one exact language. This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models in this setting. Unlike previous works we aim to make large language models able to perform detoxification without direct fine-tuning in a given language. Experiments show that multilingual models are capable of performing multilingual style transfer. However, tested state-of-the-art models are not able to perform cross-lingual detoxification and direct fine-tuning on exact language is currently inevitable and motivating the need of further research in this direction."
}