Unsupervised Multimodal Machine Translation for Low-resource Distant Language Pairs

Abstract

Unsupervised machine translation (UMT) has recently attracted more attention from researchers, enabling models to translate when languages lack parallel corpora. However, the current works mainly consider close language pairs (e.g., English-German and English-French), and the effectiveness of visual content for distant language pairs has yet to be investigated. This article proposes an unsupervised multimodal machine translation model for low-resource distant language pairs. Specifically, we first employ adequate measures such as transliteration and re-ordering to bring distant language pairs closer together. We then use visual content to extend masked language modeling and generate visual masked language modeling for UMT. Finally, empirical experiments are conducted on our distant language pair dataset and the public Multi30k dataset. Experimental results demonstrate the superior performance of our model, with BLEU score improvements of 2.5 and 2.6 on translation for distant language pairs English-Uyghur and Chinese-Uyghur. Moreover, our model also brings remarkable results for close language pairs, improving 2.3 BLEU compared with the existing models in English-German.