  This paper describes a method for using Generative Adversarial Networks to
learn distributed representations of natural language documents. We propose a
model that is based on the recently proposed Energy-Based GAN, but instead uses
a Denoising Autoencoder as the discriminator network. Document representations
are extracted from the hidden layer of the discriminator and evaluated both
quantitatively and qualitatively.
