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arXiv · 1902.00154

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

Dinghan Shen·Asli Celikyilmaz·Yizhe Zhang·Liqun Chen·Xin Wang·Jianfeng Gao·Lawrence Carin
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Abstract

Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. In this paper, we investigate several multi-level structures to learn a VAE model to generate long, and coherent text. In particular, we use a hierarchy of stochastic layers between the encoder and decoder networks to generate more informative latent codes. We also investigate a multi-level decoder structure to learn a coherent long-term structure by generating intermediate sentence representations as high-level plan vectors. Empirical results demonstrate that a multi-level VAE model produces more coherent and less repetitive long text compared to the standard VAE models and can further mitigate the posterior-collapse issue.

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Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Jianfeng Gao, Lawrence Carin. 2019-06-19. Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models. https://arxiv.org/abs/1902.00154

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