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. In order to deal with the
challenges in performing efficient posterior inference and large-scale training
during the incorporation of latent variables, we build a neural posterior
approximator, and equip it with a reparameterization technique to estimate the
variational lower bound. Experiments on Chinese-English and English-German
translation tasks demonstrate that the proposed model achieves significant
improvements over both the conventional and variational NMT models.

本文提出了一种新颖的变分递归神经机器翻译（VRNMT）模型，该模型引入了一系列潜在随机变量以发挥生成效果，这些变量是通过变分自编码器获得的，并且在 NMT 解码器的隐藏状态中包含。使用神经后验近似器和重参数化技术，该模型在汉英和英德翻译任务上实验，取得显着的性能提升。