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Variational autoencoders (VAEs) have received much attention recently as anend-to-end architecture for text generation with latent variables. In thispaper, we investigate several multi-level structures to learn a VAE model togenerate long, and coherent text. In particular, we use a hierarchy ofstochastic layers between the encoder and decoder networks to generate moreinformative latent codes. We also investigate a multi-level decoder structureto learn a coherent long-term structure by generating intermediate sentencerepresentations as high-level plan vectors. Empirical results demonstrate thata multi-level VAE model produces more coherent and less repetitive long textcompared to the standard VAE models and can further mitigate theposterior-collapse issue.

Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models