  Advances in neural variational inference have facilitated the learning of
powerful directed graphical models with continuous latent variables, such as
variational autoencoders. The hope is that such models will learn to represent
rich, multi-modal latent factors in real-world data, such as natural language
text. However, current models often assume simplistic priors on the latent
variables - such as the uni-modal Gaussian distribution - which are incapable
of representing complex latent factors efficiently. To overcome this
restriction, we propose the simple, but highly flexible, piecewise constant
distribution. This distribution has the capacity to represent an exponential
number of modes of a latent target distribution, while remaining mathematically
tractable. Our results demonstrate that incorporating this new latent
distribution into different models yields substantial improvements in natural
language processing tasks such as document modeling and natural language
generation for dialogue.
