Low-resource Neural Machine Translation with Cross-modal Alignment

How to achieve neural machine translation with limited parallel data? Existing techniques often rely on large-scale monolingual corpora, which is impractical for some low-resource languages. In this paper, we turn to connect several low-resource languages to a particular high-resource one by additional visual modality. Specifically, we propose a cross-modal contrastive learning method to learn a shared space for all languages, where both a coarsegrained sentence-level objective and a finegrained token-level one are introduced. Experimental results and further analysis show that our method can effectively learn the crossmodal and cross-lingual alignment with a small amount of image-text pairs and achieves significant improvements over the text-only baseline under both zero-shot and few-shot scenarios. Our code could be found at https: //github.com/ictnlp/LNMT-CA .