@inproceedings{maimaitituoheti-etal-2022-prompt,
    title = "A Prompt Based Approach for Euphemism Detection",
    author = "Maimaitituoheti, Abulimiti  and
      Yong, Yang  and
      Xiaochao, Fan",
    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://aclanthology.org/2022.flp-1.2",
    doi = "10.18653/v1/2022.flp-1.2",
    pages = "8--12",
    abstract = "Euphemism is an indirect way to express sensitive topics. People can comfortably communicate with each other about sensitive topics or taboos by using euphemisms. The Euphemism Detection Shared Task in the Third Workshop on Figurative Language Processing co-located with EMNLP 2022 provided a euphemism detection dataset that was divided into the train set and the test set. We made euphemism detection experiments by prompt tuning pre-trained language models on the dataset. We used RoBERTa as the pre-trained language model and created suitable templates and verbalizers for the euphemism detection task. Our approach achieved the third-best score in the euphemism detection shared task. This paper describes our model participating in the task.",
}