@inproceedings{nogueira-dos-santos-etal-2018-fighting,
    title = "Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer",
    author = "Nogueira dos Santos, Cicero  and
      Melnyk, Igor  and
      Padhi, Inkit",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/P18-2031/",
    doi = "10.18653/v1/P18-2031",
    pages = "189--194",
    abstract = "We introduce a new approach to tackle the problem of offensive language in online social media. Our approach uses unsupervised text style transfer to translate offensive sentences into non-offensive ones. We propose a new method for training encoder-decoders using non-parallel data that combines a collaborative classifier, attention and the cycle consistency loss. Experimental results on data from Twitter and Reddit show that our method outperforms a state-of-the-art text style transfer system in two out of three quantitative metrics and produces reliable non-offensive transferred sentences."
}