This thesis presents CycleNLPGAN, a deep learning architecture that introduces an innovative approach to sentence encoding and alignment. It allows the definition of a latent vector space shared across a pair of languages. The model is jointly trained to perform neural machine translation from a source language A to target language B. It generates a shared aligned vector space suitable for machine translation from a source language to a target one and vice versa. The architecture is based on a CycleGAN, a Computer Vision model that address image-to-image translation using cycle-consistent dynamics. It enforces the robustness of the resulting model and the quality of produced data.