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        <title>A Visual Attention Grounding Neural Model for Multimodal Machine Translation</title>
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        <namePart type="given">Mingyang</namePart>
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        <namePart type="given">Runxiang</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T A Visual Attention Grounding Neural Model for Multimodal Machine Translation
%A Zhou, Mingyang
%A Cheng, Runxiang
%A Lee, Yong Jae
%A Yu, Zhou
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zhou-etal-2018-visual
%X 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.
%R 10.18653/v1/D18-1400
%U https://aclanthology.org/D18-1400
%U https://doi.org/10.18653/v1/D18-1400
%P 3643-3653

Markdown (Informal)

[A Visual Attention Grounding Neural Model for Multimodal Machine Translation](https://aclanthology.org/D18-1400) (Zhou et al., EMNLP 2018)

• A Visual Attention Grounding Neural Model for Multimodal Machine Translation (Zhou et al., EMNLP 2018)

ACL

• Mingyang Zhou, Runxiang Cheng, Yong Jae Lee, and Zhou Yu. 2018. A Visual Attention Grounding Neural Model for Multimodal Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3643–3653, Brussels, Belgium. Association for Computational Linguistics.