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.

本文介绍了如何使用神经变分推断学习具有离散随机变量的神经主题模型，该模型不依赖于随机反向传播处理离散变量，结合主题模型分析整个文档的话题连贯性。使用神经变分推断，在多个语料库中，我们展示了改进的困惑度和文档理解，考虑了先验参数的影响，同时证明了本方法在话题质量的自动度量上具有竞争力和超越性能。