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

Learning to Generate Multiple Style Transfer Outputs for an Input Sentence