Unsupervised Text Style Transfer with Deep Generative Models
Zhongtao Jiang Yuanzhe Zhang Yiming Ju Kang Liu
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.