  In this paper, we propose a model using generative adversarial net (GAN) to
generate realistic text. Instead of using standard GAN, we combine variational
autoencoder (VAE) with generative adversarial net. The use of high-level latent
random variables is helpful to learn the data distribution and solve the
problem that generative adversarial net always emits the similar data. We
propose the VGAN model where the generative model is composed of recurrent
neural network and VAE. The discriminative model is a convolutional neural
network. We train the model via policy gradient. We apply the proposed model to
the task of text generation and compare it to other recent neural network based
models, such as recurrent neural network language model and SeqGAN. We evaluate
the performance of the model by calculating negative log-likelihood and the
BLEU score. We conduct experiments on three benchmark datasets, and results
show that our model outperforms other previous models.

Computation and Language