  The Generative Adversarial Network (GAN) has achieved great success in
generating realistic (real-valued) synthetic data. However, convergence issues
and difficulties dealing with discrete data hinder the applicability of GAN to
text. We propose a framework for generating realistic text via adversarial
training. We employ a long short-term memory network as generator, and a
convolutional network as discriminator. Instead of using the standard objective
of GAN, we propose matching the high-dimensional latent feature distributions
of real and synthetic sentences, via a kernelized discrepancy metric. This
eases adversarial training by alleviating the mode-collapsing problem. Our
experiments show superior performance in quantitative evaluation, and
demonstrate that our model can generate realistic-looking sentences.
