@inproceedings{hahn-etal-2021-modeling,
    title = "Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces",
    author = "Hahn, Vanessa  and
      Ruiter, Dana  and
      Kleinbauer, Thomas  and
      Klakow, Dietrich",
    editor = "Mostafazadeh Davani, Aida  and
      Kiela, Douwe  and
      Lambert, Mathias  and
      Vidgen, Bertie  and
      Prabhakaran, Vinodkumar  and
      Waseem, Zeerak",
    booktitle = "Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.woah-1.2",
    doi = "10.18653/v1/2021.woah-1.2",
    pages = "6--16",
    abstract = "Hate speech and profanity detection suffer from data sparsity, especially for languages other than English, due to the subjective nature of the tasks and the resulting annotation incompatibility of existing corpora. In this study, we identify profane subspaces in word and sentence representations and explore their generalization capability on a variety of similar and distant target tasks in a zero-shot setting. This is done monolingually (German) and cross-lingually to closely-related (English), distantly-related (French) and non-related (Arabic) tasks. We observe that, on both similar and distant target tasks and across all languages, the subspace-based representations transfer more effectively than standard BERT representations in the zero-shot setting, with improvements between F1 +10.9 and F1 +42.9 over the baselines across all tested monolingual and cross-lingual scenarios.",
}