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.

本文提出了一种基于对比学习范式的新型文本风格转换模型，通过明确收集相似语义句子和设计基于孪生模型的风格分类器，以解决文本样式转换中的内容迁移和样式歧义等问题。针对这些问题，实验结果表明，该模型比现有技术更加有效。