Based on multilingual pre-trained models, cross-lingual transfer with prompt
learning has shown promising effectiveness, where soft prompt learned in a
source language is transferred to target languages for downstream tasks,
particularly in the low-resource scenario. To efficiently transfer soft prompt,
we propose a novel framework, Multilingual Prompt Translator (MPT), where a
multilingual prompt translator is introduced to properly process crucial
knowledge embedded in prompt by changing language knowledge while retaining
task knowledge. Concretely, we first train prompt in source language and employ
translator to translate it into target prompt. Besides, we extend an external
corpus as auxiliary data, on which an alignment task for predicted answer
probability is designed to convert language knowledge, thereby equipping target
prompt with multilingual knowledge. In few-shot settings on XNLI, MPT
demonstrates superiority over baselines by remarkable improvements. MPT is more
prominent compared with vanilla prompting when transferring to languages quite
distinct from source language.

基于多语言预训练模型，通过提示学习的跨语言转移已显示出有希望的有效性，其中在低资源情景中，从源语言到目标语言的软提示转移到下游任务中。为了有效地转移软提示，我们提出了一个新颖的框架 —— 多语言提示翻译器（MPT），通过引入多语言提示翻译器来适当处理嵌入在提示中的重要知识，同时保留任务知识。在 XNLI 的少样本设置中，MPT 表现出了比基线方法更突出的改进，当转移到与源语言相当不同的语言时，MPT 比普通提示更为突出。