  Variational autoencoders (VAEs) are essential tools in end-to-end
representation learning. However, the sequential text generation common pitfall
with VAEs is that the model tends to ignore latent variables with a strong
auto-regressive decoder. In this paper, we propose a principled approach to
alleviate this issue by applying a discretized bottleneck to enforce an
implicit latent feature matching in a more compact latent space. We impose a
shared discrete latent space where each input is learned to choose a
combination of latent atoms as a regularized latent representation. Our model
endows a promising capability to model underlying semantics of discrete
sequences and thus provide more interpretative latent structures. Empirically,
we demonstrate our model's efficiency and effectiveness on a broad range of
tasks, including language modeling, unaligned text style transfer, dialog
response generation, and neural machine translation.
