  Generative Adversarial Networks (GANs) have been promising in the field of
image generation, however, they have been hard to train for language
generation. GANs were originally designed to output differentiable values, so
discrete language generation is challenging for them which causes high levels
of instability in training GANs. Consequently, past work has resorted to
pre-training with maximum-likelihood or training GANs without pre-training with
a WGAN objective with a gradient penalty. In this study, we present a
comparison of those approaches. Furthermore, we present the results of some
experiments that indicate better training and convergence of Wasserstein GANs
(WGANs) when a weaker regularization term is enforcing the Lipschitz
constraint.
