  Deep latent variable models, trained using variational autoencoders or
generative adversarial networks, are now a key technique for representation
learning of continuous structures. However, applying similar methods to
discrete structures, such as text sequences or discretized images, has proven
to be more challenging. In this work, we propose a flexible method for training
deep latent variable models of discrete structures. Our approach is based on
the recently-proposed Wasserstein autoencoder (WAE) which formalizes the
adversarial autoencoder (AAE) as an optimal transport problem. We first extend
this framework to model discrete sequences, and then further explore different
learned priors targeting a controllable representation. This adversarially
regularized autoencoder (ARAE) allows us to generate natural textual outputs as
well as perform manipulations in the latent space to induce change in the
output space. Finally we show that the latent representation can be trained to
perform unaligned textual style transfer, giving improvements both in
automatic/human evaluation compared to existing methods.
