  Prompting shows promising results in few-shot scenarios. However, its
strength for multilingual/cross-lingual problems has not been fully exploited.
Zhao and Sch\"utze (2021) made initial explorations in this direction by
presenting that cross-lingual prompting outperforms cross-lingual finetuning.
In this paper, we conduct an empirical exploration on the effect of each
component in cross-lingual prompting and derive language-agnostic Universal
Prompting, which helps alleviate the discrepancies between source-language
training and target-language inference. Based on this, we propose DPA, a dual
prompt augmentation framework, aiming at relieving the data scarcity issue in
few-shot cross-lingual prompting. Notably, for XNLI, our method achieves 46.54%
with only 16 English training examples per class, significantly better than
34.99% of finetuning. Our code is available at
https://github.com/DAMO-NLP-SG/DPA.
