@inproceedings{ma-etal-2026-diff4tst,
title = "{D}iff4{TST}: Masked Diffusion Language Model for Text Style Transfer",
author = "Ma, Xinchen and
He, Gaole and
Lan, Yunshi and
Qian, Weining",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.306/",
pages = "6736--6748",
ISBN = "979-8-89176-390-6",
abstract = "Despite recent progress in LLMs for text style transfer, most existing methods rely on costly task-specific training and offer limited control over separating stylistic modification from content preservation. We propose Diff4TST, a diffusion-based language model that formulates text style transfer as an explicit copy-and-edit process. Built upon masked diffusion language models, Diff4TST introduces a style-aware noise schedule that selectively perturbs stylistic tokens while preserving content-bearing tokens during supervised fine-tuning.At inference time, we further introduce a generate-then-refine strategy that iteratively improves style compliance via gradient-based token re-masking, without reinforcement learning or external reward models. Extensive experiments on both fine-grained and polarity-based benchmarks show that Diff4TST achieves substantially improved style accuracy and controllability while maintaining strong content preservation and fluency. These results suggest diffusion-based language models as a principled and effective alternative to autoregressive pipelines for text style transfer."
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%0 Conference Proceedings
%T Diff4TST: Masked Diffusion Language Model for Text Style Transfer
%A Ma, Xinchen
%A He, Gaole
%A Lan, Yunshi
%A Qian, Weining
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F ma-etal-2026-diff4tst
%X Despite recent progress in LLMs for text style transfer, most existing methods rely on costly task-specific training and offer limited control over separating stylistic modification from content preservation. We propose Diff4TST, a diffusion-based language model that formulates text style transfer as an explicit copy-and-edit process. Built upon masked diffusion language models, Diff4TST introduces a style-aware noise schedule that selectively perturbs stylistic tokens while preserving content-bearing tokens during supervised fine-tuning.At inference time, we further introduce a generate-then-refine strategy that iteratively improves style compliance via gradient-based token re-masking, without reinforcement learning or external reward models. Extensive experiments on both fine-grained and polarity-based benchmarks show that Diff4TST achieves substantially improved style accuracy and controllability while maintaining strong content preservation and fluency. These results suggest diffusion-based language models as a principled and effective alternative to autoregressive pipelines for text style transfer.
%U https://aclanthology.org/2026.acl-long.306/
%P 6736-6748
Markdown (Informal)
[Diff4TST: Masked Diffusion Language Model for Text Style Transfer](https://aclanthology.org/2026.acl-long.306/) (Ma et al., ACL 2026)
ACL
- Xinchen Ma, Gaole He, Yunshi Lan, and Weining Qian. 2026. Diff4TST: Masked Diffusion Language Model for Text Style Transfer. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6736–6748, San Diego, California, United States. Association for Computational Linguistics.