We present a syntax-infused variational autoencoder (SIVAE), that integrates
sentences with their syntactic trees to improve the grammar of generated
sentences. Distinct from existing VAE-based text generative models, SIVAE
contains two separate latent spaces, for sentences and syntactic trees. The
evidence lower bound objective is redesigned correspondingly, by optimizing a
joint distribution that accommodates two encoders and two decoders. SIVAE works
with long short-term memory architectures to simultaneously generate sentences
and syntactic trees. Two versions of SIVAE are proposed: one captures the
dependencies between the latent variables through a conditional prior network,
and the other treats the latent variables independently such that
syntactically-controlled sentence generation can be performed. Experimental
results demonstrate the generative superiority of SIVAE on both reconstruction
and targeted syntactic evaluations. Finally, we show that the proposed models
can be used for unsupervised paraphrasing given different syntactic tree
templates.

本研究提出了一种语法融入变分自编码器（SIVAE），通过集成句法树来提高生成句子的语法，在生成模型上有独立的潜变量空间，可以用于句法控制的句子生成和无监督的改写任务。