  Generating realistic sequences is a central task in many machine learning
applications. There has been considerable recent progress on building deep
generative models for sequence generation tasks. However, the issue of
mode-collapsing remains a main issue for the current models. In this paper we
propose a GAN-based generic framework to address the problem of mode-collapse
in a principled approach. We change the standard GAN objective to maximize a
variational lower-bound of the log-likelihood while minimizing the
Jensen-Shanon divergence between data and model distributions. We experiment
our model with text generation task and show that it can generate realistic
text with high diversity.
