Abstract
Variational auto-encoders (VAEs) are widely
used in natural language generation due to
the regularization of the latent space. However, generating sentences from the continuous latent space does not explicitly model the
syntactic information. In this paper, we propose to generate sentences from disentangled
syntactic and semantic spaces. Our proposed
method explicitly models syntactic information in the VAE’s latent space by using the linearized tree sequence, leading to better performance of language generation. Additionally,
the advantage of sampling in the disentangled
syntactic and semantic latent spaces enables us
to perform novel applications, such as the unsupervised paraphrase generation and syntaxtransfer generation. Experimental results show
that our proposed model achieves similar or
better performance in various tasks, compared
with state-of-the-art related work. ‡