  The variational autoencoder (VAE) can learn the manifold of natural images on
certain datasets, as evidenced by meaningful interpolating or extrapolating in
the continuous latent space. However, on discrete data such as text, it is
unclear if unsupervised learning can discover similar latent space that allows
controllable manipulation. In this work, we find that sequence VAEs trained on
text fail to properly decode when the latent codes are manipulated, because the
modified codes often land in holes or vacant regions in the aggregated
posterior latent space, where the decoding network fails to generalize. Both as
a validation of the explanation and as a fix to the problem, we propose to
constrain the posterior mean to a learned probability simplex, and performs
manipulation within this simplex. Our proposed method mitigates the latent
vacancy problem and achieves the first success in unsupervised learning of
controllable representations for text. Empirically, our method outperforms
unsupervised baselines and strong supervised approaches on text style transfer,
and is capable of performing more flexible fine-grained control over text
generation than existing methods.
