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.

本文发现，序列变分自编码器（VAE）在文本数据上训练时，由于修改后的编码值往往落在聚合后的后验潜空间中的空洞或空缺区域，导致译码网络无法推广，因此无法正确解码并控制操纵。为此我们建议在学习到的概率单纯形上约束后验均值，并在其内进行操作，以解决潜空缺陷问题，并在文本风格转移上取得了成功。