  We propose a method to train generative adversarial networks on mutivariate
feature vectors representing multiple categorical values. In contrast to the
continuous domain, where GAN-based methods have delivered considerable results,
GANs struggle to perform equally well on discrete data. We propose and compare
several architectures based on multiple (Gumbel) softmax output layers taking
into account the structure of the data. We evaluate the performance of our
architecture on datasets with different sparsity, number of features, ranges of
categorical values, and dependencies among the features. Our proposed
architecture and method outperforms existing models.
