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Multimodal Machine Translation (MMT) focuses on enhancing text-onlytranslation with visual features, which has attracted considerable attentionfrom both natural language processing and computer vision communities. Recentadvances still struggle to train a separate model for each language pair, whichis costly and unaffordable when the number of languages increases in the realworld. In other words, the multilingual multimodal machine translation(Multilingual MMT) task has not been investigated, which aims to handle theaforementioned issues by providing a shared semantic space for multiplelanguages. Besides, the image modality has no language boundaries, which issuperior to bridging the semantic gap between languages. To this end, we firstpropose the Multilingual MMT task by establishing two new Multilingual MMTbenchmark datasets covering seven languages. Then, an effective baseline LVP-M3using visual prompts is proposed to support translations between differentlanguages, which includes three stages (token encoding, language-aware visualprompt generation, and language translation). Extensive experimental results onour constructed benchmark datasets demonstrate the effectiveness of LVP-M3method for Multilingual MMT.

LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation