Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting

Understand

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.