@inproceedings{schick-etal-2020-automatically,
    title = "Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification",
    author = {Schick, Timo  and
      Schmid, Helmut  and
      Sch{\"u}tze, Hinrich},
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.coling-main.488/",
    doi = "10.18653/v1/2020.coling-main.488",
    pages = "5569--5578",
    abstract = "A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels. Manually defining this mapping between words and labels requires both domain expertise and an understanding of the language model{'}s abilities. To mitigate this issue, we devise an approach that automatically finds such a mapping given small amounts of training data. For a number of tasks, the mapping found by our approach performs almost as well as hand-crafted label-to-word mappings."
}