@inproceedings{tiwari-parde-2022-exploration,
    title = "An Exploration of Linguistically-Driven and Transfer Learning Methods for Euphemism Detection",
    author = "Tiwari, Devika  and
      Parde, Natalie",
    editor = "Ghosh, Debanjan  and
      Beigman Klebanov, Beata  and
      Muresan, Smaranda  and
      Feldman, Anna  and
      Poria, Soujanya  and
      Chakrabarty, Tuhin",
    booktitle = "Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.flp-1.18/",
    doi = "10.18653/v1/2022.flp-1.18",
    pages = "131--136",
    abstract = "Euphemisms are often used to drive rhetoric, but their automated recognition and interpretation are under-explored. We investigate four methods for detecting euphemisms in sentences containing potentially euphemistic terms. The first three linguistically-motivated methods rest on an understanding of (1) euphemism{'}s role to attenuate the harsh connotations of a taboo topic and (2) euphemism{'}s metaphorical underpinnings. In contrast, the fourth method follows recent innovations in other tasks and employs transfer learning from a general-domain pre-trained language model. While the latter method ultimately (and perhaps surprisingly) performed best (F1 = 0.74), we comprehensively evaluate all four methods to derive additional useful insights from the negative results."
}