Learning to Generate Multiple Style Transfer Outputs for an Input Sentence

• URL: http://arxiv.org/abs/2002.06525v1
• Date: Sun, 16 Feb 2020 07:10:45 GMT
• Title: Learning to Generate Multiple Style Transfer Outputs for an Input Sentence
• Authors: Kevin Lin, Ming-Yu Liu, Ming-Ting Sun, Jan Kautz
• Abstract summary: We propose a one-to-many text style transfer framework to generate different style transfer results for a given input text. We decompose the latent representation of the input sentence to a style code that captures the language style variation. By combining the same content code with a different style code, we generate a different style transfer output.
• Score: 93.32432042381572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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