Recent advances in neural variational inference have spawned a renaissance in
deep latent variable models. In this paper we introduce a generic variational
inference framework for generative and conditional models of text. While
traditional variational methods derive an analytic approximation for the
intractable distributions over latent variables, here we construct an inference
network conditioned on the discrete text input to provide the variational
distribution. We validate this framework on two very different text modelling
applications, generative document modelling and supervised question answering.
Our neural variational document model combines a continuous stochastic document
representation with a bag-of-words generative model and achieves the lowest
reported perplexities on two standard test corpora. The neural answer selection
model employs a stochastic representation layer within an attention mechanism
to extract the semantics between a question and answer pair. On two question
answering benchmarks this model exceeds all previous published benchmarks.

通过引入一个通用变分推断框架来构建生成式和条件文本模型的变分推断网络，我们验证了该框架在生成式文档建模和监督式问题回答两个不同的文本建模应用中的有效性。