  Generative adversarial networks (GANs) have achieved significant success in
generating real-valued data. However, the discrete nature of text hinders the
application of GAN to text-generation tasks. Instead of using the standard GAN
objective, we propose to improve text-generation GAN via a novel approach
inspired by optimal transport. Specifically, we consider matching the latent
feature distributions of real and synthetic sentences using a novel metric,
termed the feature-mover's distance (FMD). This formulation leads to a highly
discriminative critic and easy-to-optimize objective, overcoming the
mode-collapsing and brittle-training problems in existing methods. Extensive
experiments are conducted on a variety of tasks to evaluate the proposed model
empirically, including unconditional text generation, style transfer from
non-parallel text, and unsupervised cipher cracking. The proposed model yields
superior performance, demonstrating wide applicability and effectiveness.
