Cross-Lingual Transfer for Natural Language Inference via Multilingual Prompt Translator
This addresses cross-lingual NLP tasks for low-resource languages, but it is incremental as it builds on existing multilingual pre-trained models and prompt learning methods.
The paper tackles cross-lingual transfer for natural language inference by proposing a Multilingual Prompt Translator (MPT) to efficiently transfer soft prompts from source to target languages, achieving remarkable improvements in few-shot settings on XNLI.
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.