  Generative Adversarial Networks (GAN) have limitations when the goal is to
generate sequences of discrete elements. The reason for this is that samples
from a distribution on discrete objects such as the multinomial are not
differentiable with respect to the distribution parameters. This problem can be
avoided by using the Gumbel-softmax distribution, which is a continuous
approximation to a multinomial distribution parameterized in terms of the
softmax function. In this work, we evaluate the performance of GANs based on
recurrent neural networks with Gumbel-softmax output distributions in the task
of generating sequences of discrete elements.
