  Generative Adversarial Networks (GANs) have experienced a recent surge in
popularity, performing competitively in a variety of tasks, especially in
computer vision. However, GAN training has shown limited success in natural
language processing. This is largely because sequences of text are discrete,
and thus gradients cannot propagate from the discriminator to the generator.
Recent solutions use reinforcement learning to propagate approximate gradients
to the generator, but this is inefficient to train. We propose to utilize an
autoencoder to learn a low-dimensional representation of sentences. A GAN is
then trained to generate its own vectors in this space, which decode to
realistic utterances. We report both random and interpolated samples from the
generator. Visualization of sentence vectors indicate our model correctly
learns the latent space of the autoencoder. Both human ratings and BLEU scores
show that our model generates realistic text against competitive baselines.

Computation and Language;Machine Learning;Machine Learning