Low-resource Neural Machine Translation with Cross-modal Alignment

• URL: http://arxiv.org/abs/2210.06716v1
• Date: Thu, 13 Oct 2022 04:15:43 GMT
• Title: Low-resource Neural Machine Translation with Cross-modal Alignment
• Authors: Zhe Yang, Qingkai Fang, Yang Feng
• Abstract summary: We propose a cross-modal contrastive learning method to learn a shared space for all languages. 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.
• Score: 15.416659725808822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 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.

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