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.

翻译：我们提出了一种基于深度生成模型的无监督文本风格迁移通用框架。该框架将非平行语料中的每个句子-标签对视为从一个完整四元组中部分观测得到，该四元组额外包含两个分别表示内容与风格的潜码。通过挖掘观测数据内部的依赖关系学习这些潜码，进而通过操控潜码实现句子风格迁移。本框架能够将先前的嵌入方法和原型方法统一为两种特殊形式，并为该领域此前提出的对齐编码器和对抗训练等技术提供了原理性解释。我们在三个基准数据集上开展实验，自动评估与人工评估结果表明，相较于多个强基线方法，我们的方法取得了更优或相当的性能。