Cross-lingual natural language inference (XNLI) is a fundamental task in cross-lingual natural language understanding. Recently this task is commonly addressed by pre-trained cross-lingual language models. Existing methods usually enhance pre-trained language models with additional data, such as annotated parallel corpora. These additional data, however, are rare in practice, especially for low-resource languages. Inspired by recent promising results achieved by prompt-learning, this paper proposes a novel prompt-learning based framework for enhancing XNLI. It reformulates the XNLI problem to a masked language modeling problem by constructing cloze-style questions through cross-lingual templates. To enforce correspondence between different languages, the framework augments a new question for every question using a sampled template in another language and then introduces a consistency loss to make the answer probability distribution obtained from the new question as similar as possible with the corresponding distribution obtained from the original question. Experimental results on two benchmark datasets demonstrate that XNLI models enhanced by our proposed framework significantly outperform original ones under both the full-shot and few-shot cross-lingual transfer settings.