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)

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