Unsupervised multimodal machine translation (UMMT) aims to leverage vision information
as a pivot between two languages to achieve better performance on low-resource language
pairs. However, there is presently a challenge: how to handle alignment between distant
language pairs (DLPs) in UMMT. To this end, this paper proposes a visual pivoting UMMT
method for DLPs. Specifically, we first construct a dataset containing two DLPs, including
English-Uyghur and Chinese-Uyghur. We then apply the visual pivoting method for both to …

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 (eg, 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 …