  Although GAN-based methods have received many achievements in the last few
years, they have not been entirelysuccessful in generating discrete data. The
most crucial challenge of these methods is the difficulty of passing the
gradientfrom the discriminator to the generator when the generator outputs are
discrete. Despite the fact that several attemptshave been made to alleviate
this problem, none of the existing GAN-based methods have improved the
performance oftext generation compared with the maximum likelihood approach in
terms of both the quality and the diversity. In thispaper, we proposed a new
framework for generating discrete data by an adversarial approach in which
there is no need topass the gradient to the generator. The proposed method has
an iterative manner in which each new generator is definedbased on the last
discriminator. It leverages the discreteness of data and the last discriminator
to model the real datadistribution implicitly. Moreover, the method is
supported with theoretical guarantees, and experimental results generallyshow
the superiority of the proposed DGSAN method compared to the other popular or
recent methods in generatingdiscrete sequential data.
