A Visual Attention Grounding Neural Model For Multimodal Machine Translation
Mingyang Zhou, Runxiang Cheng, Yong Jae Lee, Zhou Yu . Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing 2018 – 90 citations
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.