Learning to Generate Multiple Style Transfer Outputs for an Input Sentence
Kevin Lin, Ming-Yu Liu, Ming–Ting Sun, Jan Kautz · 2020
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.