  Text style transfer aims to change the style of sentences while preserving
the semantic meanings. Due to the lack of parallel data, the Denoising
Auto-Encoder (DAE) is widely used in this task to model distributions of
different sentence styles. However, because of the conflict between the target
of the conventional denoising procedure and the target of style transfer task,
the vanilla DAE can not produce satisfying enough results. To improve the
transferability of the model, most of the existing works combine DAE with
various complicated unsupervised networks, which makes the whole system become
over-complex. In this work, we design a novel DAE model named Style-Enhanced
DAE (SE-DAE), which is specifically designed for the text style transfer task.
Compared with previous complicated style-transfer models, our model do not
consist of any complicated unsupervised networks, but only relies on the
high-quality pseudo-parallel data generated by a novel data refinement
mechanism. Moreover, to alleviate the conflict between the targets of the
conventional denoising procedure and the style transfer task, we propose
another novel style denoising mechanism, which is more compatible with the
target of the style transfer task. We validate the effectiveness of our model
on two style benchmark datasets. Both automatic evaluation and human evaluation
show that our proposed model is highly competitive compared with previous
strong the state of the art (SOTA) approaches and greatly outperforms the
vanilla DAE.
