@inproceedings{schick-schutze-2021-exploiting,
    title = "Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference",
    author = {Schick, Timo  and
      Sch{\"u}tze, Hinrich},
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.eacl-main.20/",
    doi = "10.18653/v1/2021.eacl-main.20",
    pages = "255--269",
    abstract = "Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with {\textquotedblleft}task descriptions{\textquotedblright} in natural language (e.g., Radford et al., 2019). While this approach underperforms its supervised counterpart, we show in this work that the two ideas can be combined: We introduce Pattern-Exploiting Training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. These phrases are then used to assign soft labels to a large set of unlabeled examples. Finally, standard supervised training is performed on the resulting training set. For several tasks and languages, PET outperforms supervised training and strong semi-supervised approaches in low-resource settings by a large margin."
}