  Text style transfer aims to alter the style of a sentence while preserving
its content. Due to the lack of parallel corpora, most recent work focuses on
unsupervised methods and often uses cycle construction to train models. Since
cycle construction helps to improve the style transfer ability of the model by
rebuilding transferred sentences back to original-style sentences, it brings
about a content loss in unsupervised text style transfer tasks. In this paper,
we propose a novel disentanglement-based style transfer model StyleFlow to
enhance content preservation. Instead of the typical encoder-decoder scheme,
StyleFlow can not only conduct the forward process to obtain the output, but
also infer to the input through the output. We design an attention-aware
coupling layers to disentangle the content representations and the style
representations of a sentence. Besides, we propose a data augmentation method
based on Normalizing Flow to improve the robustness of the model. Experiment
results demonstrate that our model preserves content effectively and achieves
the state-of-the-art performance on the most metrics.
