Cross-lingual natural language inference is a fundamental problem in cross-lingual language understanding. Many recent works have used prompt learning to address the lack of annotated parallel corpora in XNLI. However, these methods adopt discrete prompting by simply translating the templates to the target language and need external expert knowledge to design the templates. Besides, discrete prompts of human-designed template words are not trainable vectors and can not be migrated to target languages in the inference stage flexibly. In this paper, we propose a novel Soft prompt learning framework with the Multilingual Verbalizer (SoftMV) for XNLI. SoftMV first constructs cloze-style question with soft prompts for the input sample. Then we leverage bilingual dictionaries to generate an augmented multilingual question for the original question. SoftMV adopts a multilingual verbalizer to align the representations of original and augmented multilingual questions into the same semantic space with consistency regularization. Experimental results on XNLI demonstrate that SoftMV can achieve state-of-the-art performance and significantly outperform the previous methods under the few-shot and full-shot cross-lingual transfer settings.

翻译：跨语言自然语言推理是跨语言语言理解中的基本问题。近期许多工作采用提示学习来应对跨语言自然语言推理（XNLI）中标注平行语料匮乏的问题。然而，这些方法采用离散提示，仅通过将模板翻译成目标语言，且需借助外部专家知识设计模板。此外，人工设计的离散提示词非可训练向量，无法在推理阶段灵活迁移至目标语言。本文提出基于多语言动词化器（SoftMV）的新型软提示学习框架用于XNLI。SoftMV首先为输入样本构建含软提示的完形填空式问题，进而利用双语词典为原问题生成增强的多语言问句。SoftMV采用多语言动词化器，通过一致性正则化将原问句与增强多语言问句的表示对齐至同一语义空间。在XNLI数据集上的实验结果表明，SoftMV在少样本和全样本跨语言迁移设置下均能取得最优性能，显著优于现有方法。