@inproceedings{niederreiter-gromann-2025-word,
    title = "Word-Level Detection of Code-Mixed Hate Speech with Multilingual Domain Transfer",
    author = "Niederreiter, Karin  and
      Gromann, Dagmar",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1086/",
    pages = "21093--21104",
    ISBN = "979-8-89176-256-5",
    abstract = "The exponential growth of offensive language on social media tends to fuel online harassment and challenges detection mechanisms. Hate speech detection is commonly treated as a monolingual or multilingual sentence-level classification task. However, profane language tends to contain code-mixing, a combination of more than one language, which requires a more nuanced detection approach than binary classification. A general lack of available code-mixed datasets aggravates the problem. To address this issue, we propose five word-level annotated hate speech datasets, EN and DE from social networks, one subset of the DE-EN Offensive Content Detection Code-Switched Dataset, one DE-EN code-mixed German rap lyrics held-out test set, and a cross-domain held-out test set. We investigate the capacity of fine-tuned German-only, German-English bilingual, and German-English code-mixed token classification XLM-R models to generalize to code-mixed hate speech in German rap lyrics in zero-shot domain transfer as well as across different domains. The results show that bilingual fine-tuning facilitates not only the detection of code-mixed hate speech, but also neologisms, addressing the inherent dynamics of profane language use."
}