  Non-parallel text style transfer has attracted increasing research interests
in recent years. Despite successes in transferring the style based on the
encoder-decoder framework, current approaches still lack the ability to
preserve the content and even logic of original sentences, mainly due to the
large unconstrained model space or too simplified assumptions on latent
embedding space. Since language itself is an intelligent product of humans with
certain grammars and has a limited rule-based model space by its nature,
relieving this problem requires reconciling the model capacity of deep neural
networks with the intrinsic model constraints from human linguistic rules. To
this end, we propose a method called Graph Transformer based Auto Encoder
(GTAE), which models a sentence as a linguistic graph and performs feature
extraction and style transfer at the graph level, to maximally retain the
content and the linguistic structure of original sentences. Quantitative
experiment results on three non-parallel text style transfer tasks show that
our model outperforms state-of-the-art methods in content preservation, while
achieving comparable performance on transfer accuracy and sentence naturalness.
