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Variational autoencoders (VAEs) are essential tools in end-to-endrepresentation learning. However, the sequential text generation common pitfallwith VAEs is that the model tends to ignore latent variables with a strongauto-regressive decoder. In this paper, we propose a principled approach toalleviate this issue by applying a discretized bottleneck to enforce animplicit latent feature matching in a more compact latent space. We impose ashared discrete latent space where each input is learned to choose acombination of latent atoms as a regularized latent representation. Our modelendows a promising capability to model underlying semantics of discretesequences and thus provide more interpretative latent structures. Empirically,we demonstrate our model's efficiency and effectiveness on a broad range oftasks, including language modeling, unaligned text style transfer, dialogresponse generation, and neural machine translation.

Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck