  Multimodal Machine Translation (MMT) focuses on enhancing text-only
translation with visual features, which has attracted considerable attention
from both natural language processing and computer vision communities. Recent
advances still struggle to train a separate model for each language pair, which
is costly and unaffordable when the number of languages increases in the real
world. In other words, the multilingual multimodal machine translation
(Multilingual MMT) task has not been investigated, which aims to handle the
aforementioned issues by providing a shared semantic space for multiple
languages. Besides, the image modality has no language boundaries, which is
superior to bridging the semantic gap between languages. To this end, we first
propose the Multilingual MMT task by establishing two new Multilingual MMT
benchmark datasets covering seven languages. Then, an effective baseline LVP-M3
using visual prompts is proposed to support translations between different
languages, which includes three stages (token encoding, language-aware visual
prompt generation, and language translation). Extensive experimental results on
our constructed benchmark datasets demonstrate the effectiveness of LVP-M3
method for Multilingual MMT.
