Unsupervised Multi-Modal Neural Machine Translation

Yuanhang Su, Kai Fan, Nguyen Bach, C.-C. Jay Kuo, Fei Huang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 10482-10491


Unsupervised neural machine translation (UNMT) has recently achieved remarkable results [??] with only large monolingual corpora in each language. However, the uncertainty of associating target with source sentences makes UNMT theoretically an ill-posed problem. This work investigates the possibility of utilizing images for disambiguation to improve the performance of UNMT. Our assumption is intuitively based on the invariant property of image, i.e., the description of the same visual content by different languages should be approximately similar. We propose an unsupervised multi-modal machine translation (UMNMT) framework based on the language translation cycle consistency loss conditional on the image, targeting to learn the bidirectional multi-modal translation simultaneously. Through an alternate training between multi-modal and uni-modal, our inference model can translate with or without the image. On the widely used Multi30K dataset, the experimental results of our approach are significantly better than those of the text-only UNMT on the 2016 test dataset.

Related Material

author = {Su, Yuanhang and Fan, Kai and Bach, Nguyen and Kuo, C.-C. Jay and Huang, Fei},
title = {Unsupervised Multi-Modal Neural Machine Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}