@inproceedings{lee-etal-2023-feed,
    title = "{FEED} {PET}s: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms",
    author = "Lee, Patrick  and
      Shode, Iyanuoluwa  and
      Trujillo, Alain  and
      Zhao, Yuan  and
      Ojo, Olumide  and
      Plancarte, Diana  and
      Feldman, Anna  and
      Peng, Jing",
    editor = "Palmer, Alexis  and
      Camacho-collados, Jose",
    booktitle = "Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.starsem-1.38",
    doi = "10.18653/v1/2023.starsem-1.38",
    pages = "437--448",
    abstract = "Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work.",
}

Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work.
Lee, Patrick Shode, Iyanuoluwa Trujillo, Alain Zhao, Yuan Ojo, Olumide Plancarte, Diana Feldman, Anna Peng, Jing
FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms