Topic-Guided Variational Auto-Encoder for Text Generation

NAACL 2019 Wenlin WangZhe GanHongteng XuRuiyi ZhangGuoyin WangDinghan ShenChangyou ChenLawrence Carin

We propose a topic-guided variational auto-encoder (TGVAE) model for text generation. Distinct from existing variational auto-encoder (VAE) based approaches, which assume a simple Gaussian prior for latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module... (read more)

PDF Abstract

Code


No code implementations yet. Submit your code now

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.