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.
@article{arxiv.1808.08266,
title = {A Visual Attention Grounding Neural Model for Multimodal Machine Translation},
author = {Mingyang Zhou and Runxiang Cheng and Yong Jae Lee and Zhou Yu},
journal= {arXiv preprint arXiv:1808.08266},
year = {2018}
}