  Unsupervised machine translation (MT) has recently achieved impressive
results with monolingual corpora only. However, it is still challenging to
associate source-target sentences in the latent space. As people speak
different languages biologically share similar visual systems, the potential of
achieving better alignment through visual content is promising yet
under-explored in unsupervised multimodal MT (MMT). In this paper, we
investigate how to utilize visual content for disambiguation and promoting
latent space alignment in unsupervised MMT. Our model employs multimodal
back-translation and features pseudo visual pivoting in which we learn a shared
multilingual visual-semantic embedding space and incorporate visually-pivoted
captioning as additional weak supervision. The experimental results on the
widely used Multi30K dataset show that the proposed model significantly
improves over the state-of-the-art methods and generalizes well when the images
are not available at the testing time.
