@inproceedings{zhao-schutze-2021-discrete,
    title = "Discrete and Soft Prompting for Multilingual Models",
    author = {Zhao, Mengjie  and
      Sch{\"u}tze, Hinrich},
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2021.emnlp-main.672/",
    doi = "10.18653/v1/2021.emnlp-main.672",
    pages = "8547--8555",
    abstract = "It has been shown for English that discrete and soft prompting perform strongly in few-shot learning with pretrained language models (PLMs). In this paper, we show that discrete and soft prompting perform better than finetuning in multilingual cases: Crosslingual transfer and in-language training of multilingual natural language inference. For example, with 48 English training examples, finetuning obtains 33.74{\%} accuracy in crosslingual transfer, barely surpassing the majority baseline (33.33{\%}). In contrast, discrete and soft prompting outperform finetuning, achieving 36.43{\%} and 38.79{\%}. We also demonstrate good performance of prompting with training data in multiple languages other than English."
}