  We show how to learn a neural topic model with discrete random
variables---one that explicitly models each word's assigned topic---using
neural variational inference that does not rely on stochastic backpropagation
to handle the discrete variables. The model we utilize combines the expressive
power of neural methods for representing sequences of text with the topic
model's ability to capture global, thematic coherence. Using neural variational
inference, we show improved perplexity and document understanding across
multiple corpora. We examine the effect of prior parameters both on the model
and variational parameters and demonstrate how our approach can compete and
surpass a popular topic model implementation on an automatic measure of topic
quality.
