  We introduce a novel multimodal machine translation model that utilizes
parallel visual and textual information. Our model jointly optimizes the
learning of a shared visual-language embedding and a translator. The model
leverages a visual attention grounding mechanism that links the visual
semantics with the corresponding textual semantics. Our approach achieves
competitive state-of-the-art results on the Multi30K and the Ambiguous COCO
datasets. We also collected a new multilingual multimodal product description
dataset to simulate a real-world international online shopping scenario. On
this dataset, our visual attention grounding model outperforms other methods by
a large margin.
