@inproceedings{lai-etal-2021-thank,
    title = "Thank you {BART}! Rewarding Pre-Trained Models Improves Formality Style Transfer",
    author = "Lai, Huiyuan  and
      Toral, Antonio  and
      Nissim, Malvina",
    editor = "Zong, Chengqing  and
      Xia, Fei  and
      Li, Wenjie  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.acl-short.62/",
    doi = "10.18653/v1/2021.acl-short.62",
    pages = "484--494",
    abstract = "Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content. We show that fine-tuning pre-trained language (GPT-2) and sequence-to-sequence (BART) models boosts content preservation, and that this is possible even with limited amounts of parallel data. Augmenting these models with rewards that target style and content {--}the two core aspects of the task{--} we achieve a new state-of-the-art."
}