  Unsupervised neural machine translation (NMT) is a recently proposed approach
for machine translation which aims to train the model without using any labeled
data. The models proposed for unsupervised NMT often use only one shared
encoder to map the pairs of sentences from different languages to a
shared-latent space, which is weak in keeping the unique and internal
characteristics of each language, such as the style, terminology, and sentence
structure. To address this issue, we introduce an extension by utilizing two
independent encoders but sharing some partial weights which are responsible for
extracting high-level representations of the input sentences. Besides, two
different generative adversarial networks (GANs), namely the local GAN and
global GAN, are proposed to enhance the cross-language translation. With this
new approach, we achieve significant improvements on English-German,
English-French and Chinese-to-English translation tasks.
