DDM4TST: Diffusion Model for Fine-grained Text Style Transfer by Disentangled Representation

Fine-grained Text Style Transfer (FTST) aims to make targeted and precise modifications to specific stylistic components of a sentence. Existing methods typically attempt to disentangle style and content representations for FTST. However, style and content are inherently abstract, making them difficult to formalize and manipulate independently. To address this, we propose a novel framework, Disentanglement Diffusion Model for Text Style Transfer (DDM4TST) , which reformulates style transformation as either semantic or syntactic transformation. By learning disentangled representations, the model enables accurate and fine-grained style control. Specifically, we construct a feature parsing module to effectively separate semantic and syntactic representations. We then incorporate a diffusion model conditioned on these disentangled representations, allowing for fine-grained control of stylistic attributes while preserving the core content of the original sentence. This integration into the denoising process enhances the controllability and precision of the style transformation. Extensive experiments on the benchmark StylePTB dataset demonstrate that our model consistently outperforms widely adopted baselines. The results validate the effectiveness of our approach in achieving high-quality style transformation while maintaining content fidelity.