A Syntax-Aware Approach for Unsupervised Text Style Transfer

Yun Ma, Yangbin Chen, Xudong Mao, Qing Li

Abstract: Unsupervised text style transfer aims to rewrite the text of a source style into a target style while preserving the style-independent content, without parallel training corpus. Most of the existing methods address the problem by only leveraging the surface forms of words. In this paper, we incorporate the syntactic knowledge and propose a multi-task learning based Syntax-Aware Style Transfer (SAST) model. Our SAST jointly learns to generate a transferred output with aligned words and syntactic labels, where the alignment between the words and syntactic labels is enforced with a consistency constraint. The auxiliary syntactic label generation task regularizes the model to form more generalized representations, which is a desirable property especially in unsupervised tasks. Experimental results on two benchmark datasets for text style transfer demonstrate the effectiveness of the proposed method in terms of transfer accuracy, content preservation, and fluency.