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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>Multi-modal neural machine translation (NMT) aims to translate source sentences into a target language paired with images. However, dominant multi-modal NMT models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have potential to refine multi-modal representation learning. To deal with this issue, in this paper, we propose a novel graph-based multi-modal fusion encoder for NMT. Specifically, we first represent the input sentence and image using a unified multi-modal graph, which captures various semantic relationships between multi-modal semantic units (words and visual objects). We then stack multiple graph-based multi-modal fusion layers that iteratively perform semantic interactions to learn node representations. Finally, these representations provide an attention-based context vector for the decoder. We evaluate our proposed encoder on the Multi30K datasets. Experimental results and in-depth analysis show the superiority of our multi-modal NMT model.</abstract>
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%0 Conference Proceedings
%T A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation
%A Yin, Yongjing
%A Meng, Fandong
%A Su, Jinsong
%A Zhou, Chulun
%A Yang, Zhengyuan
%A Zhou, Jie
%A Luo, Jiebo
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F yin-etal-2020-novel
%X Multi-modal neural machine translation (NMT) aims to translate source sentences into a target language paired with images. However, dominant multi-modal NMT models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have potential to refine multi-modal representation learning. To deal with this issue, in this paper, we propose a novel graph-based multi-modal fusion encoder for NMT. Specifically, we first represent the input sentence and image using a unified multi-modal graph, which captures various semantic relationships between multi-modal semantic units (words and visual objects). We then stack multiple graph-based multi-modal fusion layers that iteratively perform semantic interactions to learn node representations. Finally, these representations provide an attention-based context vector for the decoder. We evaluate our proposed encoder on the Multi30K datasets. Experimental results and in-depth analysis show the superiority of our multi-modal NMT model.
%R 10.18653/v1/2020.acl-main.273
%U https://aclanthology.org/2020.acl-main.273/
%U https://doi.org/10.18653/v1/2020.acl-main.273
%P 3025-3035

Markdown (Informal)

[A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation](https://aclanthology.org/2020.acl-main.273/) (Yin et al., ACL 2020)

• A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation (Yin et al., ACL 2020)

ACL

• Yongjing Yin, Fandong Meng, Jinsong Su, Chulun Zhou, Zhengyuan Yang, Jie Zhou, and Jiebo Luo. 2020. A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3025–3035, Online. Association for Computational Linguistics.