@inproceedings{lai-etal-2022-multilingual,
    title = "Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer",
    author = "Lai, Huiyuan  and
      Toral, Antonio  and
      Nissim, Malvina",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.acl-short.29/",
    doi = "10.18653/v1/2022.acl-short.29",
    pages = "262--271",
    abstract = "We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using machine translated data as well as gold aligned English sentences yields state-of-the-art results in the three target languages we consider. Besides, in view of the general scarcity of parallel data, we propose a modular approach for multilingual formality transfer, which consists of two training strategies that target adaptation to both language and task. Our approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages."
}