Noise-robust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation

Junjie Ye, Junjun Guo, Yan Xiang, Kaiwen Tan, Zhengtao Yu


Abstract
Multi-modal neural machine translation (MNMT) aims to improve textual level machine translation performance in the presence of text-related images. Most of the previous works on MNMT focus on multi-modal fusion methods with full visual features. However, text and its corresponding image may not match exactly, visual noise is generally inevitable. The irrelevant image regions may mislead or distract the textual attention and cause model performance degradation. This paper proposes a noise-robust multi-modal interactive fusion approach with cross-modal relation-aware mask mechanism for MNMT. A text-image relation-aware attention module is constructed through the cross-modal interaction mask mechanism, and visual features are extracted based on the text-image interaction mask knowledge. Then a noise-robust multi-modal adaptive fusion approach is presented by fusion the relevant visual and textual features for machine translation. We validate our method on the Multi30K dataset. The experimental results show the superiority of our proposed model, and achieve the state-of-the-art scores in all En-De, En-Fr and En-Cs translation tasks.
Anthology ID:
2022.coling-1.452
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5098–5108
Language:
URL:
https://aclanthology.org/2022.coling-1.452
DOI:
Bibkey:
Cite (ACL):
Junjie Ye, Junjun Guo, Yan Xiang, Kaiwen Tan, and Zhengtao Yu. 2022. Noise-robust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5098–5108, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Noise-robust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation (Ye et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.452.pdf