@inproceedings{alimova-etal-2026-tatoxa,
    title = "The Tatoxa System for Text Detoxification in Low-Resource Languages: The Case of {T}atar",
    author = "Alimova, Ilseyar  and
      Monogov, Bogdan  and
      Mazur, Artyom  and
      Antonov, Daniil  and
      Karimov, Vsevolod  and
      Egorov, Vitaliy  and
      Khakimov, Bulat  and
      Panchenko, Alexander",
    editor = "Mohammad, Saif M.  and
      Ousidhoum, Nedjma",
    booktitle = "Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*{SEM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.starsem-conference.17/",
    pages = "264--274",
    ISBN = "979-8-89176-413-2",
    abstract = "Text detoxification, the automated detection and mitigation of abusive and harmful content, is essential for ensuring the safety of online communities and protecting users. However, low resource languages such as Tatar have received little research attention. In this paper we present Tatoxa, a novel state-of-the-art system for text detoxification in the Tatar language. Comparative experiments show that the proposed approach outperforms existing open source and proprietary commercial LLMs on key quality metrics. We also introduce a new dataset for text detoxification in Tatar, designed for fine tuning and evaluation in low resource settings. Finally, cross lingual transfer experiments indicate that transfer from other languages, including the culturally close Russian, performs significantly worse than training on native Tatar data even when a large Russian corpus is available."
}