  Text style transfer task requires the model to transfer a sentence of one
style to another style while retaining its original content meaning, which is a
challenging problem that has long suffered from the shortage of parallel data.
In this paper, we first propose a semi-supervised text style transfer model
that combines the small-scale parallel data with the large-scale nonparallel
data. With these two types of training data, we introduce a projection function
between the latent space of different styles and design two constraints to
train it. We also introduce two other simple but effective semi-supervised
methods to compare with. To evaluate the performance of the proposed methods,
we build and release a novel style transfer dataset that alters sentences
between the style of ancient Chinese poem and the modern Chinese.
