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Unsupervised machine translation (MT) has recently achieved impressiveresults with monolingual corpora only. However, it is still challenging toassociate source-target sentences in the latent space. As people speakdifferent languages biologically share similar visual systems, the potential ofachieving better alignment through visual content is promising yetunder-explored in unsupervised multimodal MT (MMT). In this paper, weinvestigate how to utilize visual content for disambiguation and promotinglatent space alignment in unsupervised MMT. Our model employs multimodalback-translation and features pseudo visual pivoting in which we learn a sharedmultilingual visual-semantic embedding space and incorporate visually-pivotedcaptioning as additional weak supervision. The experimental results on thewidely used Multi30K dataset show that the proposed model significantlyimproves over the state-of-the-art methods and generalizes well when the imagesare not available at the testing time.

Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting