SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer
<|reference_start|>SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer: 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: https://github.com/jiezhao6/SC2.<|reference_end|>
@article{zhao2024sc2:,
    title={SC2: Towards Enhancing Content Preservation and Style Consistency in
  Long Text Style Transfer},
    author={Jie Zhao, Ziyu Guan, Cai Xu, Wei Zhao and Yue Jiang},
    journal={arXiv preprint arXiv:2406.04578},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04578},
    primaryClass={cs.CL}
}