  Despite the dramatic success in image generation, Generative Adversarial
Networks (GANs) still face great challenges in synthesizing sequences of
discrete elements, in particular human language. The difficulty in generator
training arises from the limited representation capacity and uninformative
learning signals obtained from the discriminator. In this work, we (1) first
empirically show that the mixture-of-experts approach is able to enhance the
representation capacity of the generator for language GANs and (2) harness the
Feature Statistics Alignment (FSA) paradigm to render fine-grained learning
signals to advance the generator training. Specifically, FSA forces the mean
statistics of the distribution of fake data to approach that of real samples as
close as possible in the finite-dimensional feature space. Empirical study on
synthetic and real benchmarks shows the superior performance in quantitative
evaluation and demonstrates the effectiveness of our approach to adversarial
text generation.
