@inproceedings{hakimi-parizi-etal-2019-unbnlp,
    title = "{UNBNLP} at {S}em{E}val-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language",
    author = "Hakimi Parizi, Ali  and
      King, Milton  and
      Cook, Paul",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S19-2092/",
    doi = "10.18653/v1/S19-2092",
    pages = "514--518",
    abstract = "In this paper we apply a range of approaches to language modeling {--} including word-level n-gram and neural language models, and character-level neural language models {--} to the problem of detecting hate speech and offensive language. Our findings indicate that language models are able to capture knowledge of whether text is hateful or offensive. However, our findings also indicate that more conventional approaches to text classification often perform similarly or better."
}