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.

本研究提出了一种基于多模态机器翻译框架的无监督神经机器翻译方法，通过图像识别加强双向多模态翻译的学习效果，在 Multi30K 数据集上与传统基于文本的神经机器翻译相比，本方法有更好的实验结果。