A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation

Yongjing Yin, Fandong Meng, Jinsong Su, Chulun Zhou, Zhengyuan Yang, Jie Zhou, Jiebo Luo


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.
Anthology ID:
2020.acl-main.273
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3025–3035
Language:
URL:
https://aclanthology.org/2020.acl-main.273
DOI:
10.18653/v1/2020.acl-main.273
Bibkey:
Cite (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.
Cite (Informal):
A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation (Yin et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.273.pdf
Video:
 http://slideslive.com/38929288
Code
 middlekisser/GMNMT