  Text style transfer is a challenging text generation problem, which aims at
altering the style of a given sentence to a target one while keeping its
content unchanged. Since there is a natural scarcity of parallel datasets,
recent works mainly focus on solving the problem in an unsupervised manner.
However, previous gradient-based works generally suffer from the deficiencies
as follows, namely: (1) Content migration. Previous approaches lack explicit
modeling of content invariance and are thus susceptible to content shift
between the original sentence and the transferred one. (2) Style
misclassification. A natural drawback of the gradient-guided approaches is that
the inference process is homogeneous with a line of adversarial attack, making
latent optimization easily becomes an attack to the classifier due to
misclassification. This leads to difficulties in achieving high transfer
accuracy. To address the problems, we propose a novel gradient-guided model
through a contrastive paradigm for text style transfer, to explicitly gather
similar semantic sentences, and to design a siamese-structure based style
classifier for alleviating such two issues, respectively. Experiments on two
datasets show the effectiveness of our proposed approach, as compared to the
state-of-the-arts.
