Variational Recurrent Neural Machine Translation

Understand

Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper.

• Different from the variational NMT, VRNMT introduces a series of latent random variables to model the translation procedure of a sentence in a generative way, instead of a single latent variable.
• Specifically, the latent random variables are included into the hidden states of the NMT decoder with elements from the variational autoencoder.
• In this way, these variables are recurrently generated, which enables them to further capture strong and complex dependencies among the output translations at different timesteps.