We present a general framework for unsupervised text style transfer with deep
generative models. The framework models each sentence-label pair in the
non-parallel corpus as partially observed from a complete quadruplet which
additionally contains two latent codes representing the content and style,
respectively. These codes are learned by exploiting dependencies inside the
observed data. Then a sentence is transferred by manipulating them. Our
framework is able to unify previous embedding and prototype methods as two
special forms. It also provides a principled perspective to explain previously
proposed techniques in the field such as aligned encoder and adversarial
training. We further conduct experiments on three benchmarks. Both automatic
and human evaluation results show that our methods achieve better or
competitive results compared to several strong baselines.

我们提出了一个用深度生成模型进行无监督文本风格转换的通用框架，该框架利用观察数据中的依赖关系学习内容和风格的潜在代码，并通过操纵这些代码来转换句子。实验结果表明，与几个强基准方法相比，我们的方法在自动评估和人工评估中取得了更好或具有竞争力的结果。