How to achieve neural machine translation with limited parallel data? Existing techniques often rely on large-scale monolingual corpus, 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 coarse-grained sentence-level objective and a fine-grained token-level one are introduced. Experimental results and further analysis show that our method can effectively learn the cross-modal 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.

Low-resource Neural Machine Translation with Cross-modal Alignment

**Topics:** Interpretability, Interactivity and Analysis of Models for NLP 
Linguistic Theories, Cognitive Modeling and Psycholinguistics 
Machine Learning for NLP 
Machine Translation 
Multilinguality Demo: Model Compression

Virtual Poster Session 9

poster

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Low-resource Neural Machine Translation with Cross-modal Alignment

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