SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer
Jie Zhao, Ziyu Guan, Cai Xu, Wei Zhao, Yue Jiang · 2024
Text style transfer (TST) aims to vary the style polarity of text while preserving the semantic content.Although recent advancements have demonstrated remarkable progress in short TST, it remains a relatively straightforward task with limited practical applications.The more comprehensive long TST task presents two challenges: (1) existing methods encounter difficulties in accurately evaluating content attributes in multiple words, leading to content degradation; (2) the conventional vanilla style classifier loss encounters obstacles in maintaining consistent style across multiple generated sentences.In this paper, we propose a novel method SC2, where a multilayer Joint Style-Content Weighed (JSCW) module and a Style Consistency loss are designed to address the two issues.The JSCW simultaneously assesses the amounts of style and content attributes within a token, aiming to acquire a lossless content representation and thereby enhancing content preservation.The multiple JSCW layers further progressively refine content representations.We design a style consistency loss to ensure the generated multiple sentences consistently reflect the target style polarity.Moreover, we incorporate a denoising non-autoregressive decoder to accelerate the training.We conduct plentiful experiments and the results show significant improvements of SC2 over competitive baselines.Our code: