The standard recurrent neural network language model (RNNLM) generates
sentences one word at a time and does not work from an explicit global sentence
representation. In this work, we introduce and study an RNN-based variational
autoencoder generative model that incorporates distributed latent
representations of entire sentences. This factorization allows it to explicitly
model holistic properties of sentences such as style, topic, and high-level
syntactic features. Samples from the prior over these sentence representations
remarkably produce diverse and well-formed sentences through simple
deterministic decoding. By examining paths through this latent space, we are
able to generate coherent novel sentences that interpolate between known
sentences. We present techniques for solving the difficult learning problem
presented by this model, demonstrate its effectiveness in imputing missing
words, explore many interesting properties of the model's latent sentence
space, and present negative results on the use of the model in language
modeling.

本文介绍一种基于 RNN 和 VAE 的生成模型，该模型可以显式地模拟句子的整体特征，并通过分布式的潜在表示编码句子的风格、主题和高级语法特征。模型生成多样且完整的句子，能够插值生成连贯且新颖的句子，并用于填补缺失的词，同时探索了潜在句子空间的许多有趣属性。