  Text style transfer refers to the task of rephrasing a given text in a
different style. While various methods have been proposed to advance the state
of the art, they often assume the transfer output follows a delta distribution,
and thus their models cannot generate different style transfer results for a
given input text. To address the limitation, we propose a one-to-many text
style transfer framework. In contrast to prior works that learn a one-to-one
mapping that converts an input sentence to one output sentence, our approach
learns a one-to-many mapping that can convert an input sentence to multiple
different output sentences, while preserving the input content. This is
achieved by applying adversarial training with a latent decomposition scheme.
Specifically, we decompose the latent representation of the input sentence to a
style code that captures the language style variation and a content code that
encodes the language style-independent content. We then combine the content
code with the style code for generating a style transfer output. By combining
the same content code with a different style code, we generate a different
style transfer output. Extensive experimental results with comparisons to
several text style transfer approaches on multiple public datasets using a
diverse set of performance metrics validate effectiveness of the proposed
approach.
